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How Technology Is Changing the Future of Higher Education

Labs test artificial intelligence, virtual reality and other innovations that could improve learning and lower costs for Generation Z and beyond.

By Jon Marcus

This article is part of our latest Learning special report . We’re focusing on Generation Z, which is facing challenges from changing curriculums and new technology to financial aid gaps and homelessness.

MANCHESTER, N.H. — Cruising to class in her driverless car, a student crams from notes projected on the inside of the windshield while she gestures with her hands to shape a 3-D holographic model of her architecture project.

It looks like science fiction, an impression reinforced by the fact that it is being demonstrated in virtual reality in an ultramodern space with overstuffed pillows for seats. But this scenario is based on technology already in development.

The setting is the Sandbox ColLABorative, the innovation arm of Southern New Hampshire University, on the fifth floor of a downtown building with panoramic views of the sprawling red brick mills that date from this city’s 19th-century industrial heyday.

It is one of a small but growing number of places where experts are testing new ideas that will shape the future of a college education, using everything from blockchain networks to computer simulations to artificial intelligence, or A.I.

Theirs is not a future of falling enrollment, financial challenges and closing campuses. It’s a brighter world in which students subscribe to rather than enroll in college, learn languages in virtual reality foreign streetscapes with avatars for conversation partners, have their questions answered day or night by A.I. teaching assistants and control their own digital transcripts that record every life achievement.

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How technology is shaping learning in higher education

About the authors.

This article is a collaborative effort by Claudio Brasca, Charag Krishnan , Varun Marya , Katie Owen, Joshua Sirois, and Shyla Ziade, representing views from McKinsey’s Education Practice.

The COVID-19 pandemic forced a shift to remote learning overnight for most higher-education students, starting in the spring of 2020. To complement video lectures and engage students in the virtual classroom, educators adopted technologies that enabled more interactivity and hybrid models of online and in-person activities. These tools changed learning, teaching, and assessment in ways that may persist after the pandemic. Investors have taken note. Edtech start-ups raised record amounts of venture capital in 2020 and 2021, and market valuations for bigger players soared.

A study conducted by McKinsey in 2021 found that to engage most effectively with students, higher-education institutions can focus on eight dimensions  of the learning experience. In this article, we describe the findings of a study of the learning technologies that can enable aspects of several of those eight dimensions (see sidebar “Eight dimensions of the online learning experience”).

Eight dimensions of the online learning experience

Leading online higher-education institutions focus on eight key dimensions of the learning experience across three overarching principles.

Seamless journey

Clear education road map: “My online program provides a road map to achieve my life goals and helps me structure my day to day to achieve steady progress.”

Seamless connections: “I have one-click access to classes and learning resources in the virtual learning platform through my laptop or my phone.”

Engaging teaching approach

Range of learning formats: “My program offers a menu of engaging courses with both self-guided and real-time classes, and lots of interaction with instructors and peers.”

Captivating experiences: “I learn from the best professors and experts. My classes are high quality, with up-to-date content.”

Adaptive learning: “I access a personalized platform that helps me practice exercises and exams and gives immediate feedback without having to wait for the course teacher.”

Real-world skills application: “My online program helps me get hands-on practice using exciting virtual tools to solve real-world problems.”

Caring network

Timely support: “I am not alone in my learning journey and have adequate 24/7 support for academic and nonacademic issues.”

Strong community: “I feel part of an academic community and I’m able to make friends online.”

In November 2021, McKinsey surveyed 600 faculty members and 800 students from public and private nonprofit colleges and universities in the United States, including minority-serving institutions, about the use and impact of eight different classroom learning technologies (Exhibit 1). (For more on the learning technologies analyzed in this research, see sidebar “Descriptions of the eight learning technologies.”) To supplement the survey, we interviewed industry experts and higher-education professionals who make decisions about classroom technology use. We discovered which learning tools and approaches have seen the highest uptake, how students and educators view them, the barriers to higher adoption, how institutions have successfully adopted innovative technologies, and the notable impacts on learning (for details about our methodology, see sidebar “About the research”).

Double-digit growth in adoption and positive perceptions

Descriptions of the eight learning technologies.

• Classroom interactions: These are software platforms that allow students to ask questions, make comments, respond to polls, and attend breakout discussions in real time, among other features. They are downloadable and accessible from phones, computers, and tablets, relevant to all subject areas, and useful for remote and in-person learning.
• Classroom exercises: These platforms gamify learning with fun, low-stakes competitions, pose problems to solve during online classes, allow students to challenge peers to quizzes, and promote engagement with badges and awards. They are relevant to all subject areas.
• Connectivity and community building: A broad range of informal, opt-in tools, these allow students to engage with one another and instructors and participate in the learning community. They also include apps that give students 24/7 asynchronous access to lectures, expanded course materials, and notes with enhanced search and retrieval functionality.
• Group work: These tools let students collaborate in and out of class via breakout/study rooms, group preparation for exams and quizzes, and streamlined file sharing.
• Augmented reality/virtual reality (AR/VR): Interactive simulations immerse learners in course content, such as advanced lab simulations for hard sciences, medical simulations for nursing, and virtual exhibit tours for the liberal arts. AR can be offered with proprietary software on most mobile or laptop devices. VR requires special headsets, proprietary software, and adequate classroom space for simultaneous use.
• AI adaptive course delivery: Cloud-based, AI-powered software adapts course content to a student’s knowledge level and abilities. These are fully customizable by instructors and available in many subject areas, including business, humanities, and sciences.
• Machine learning–powered teaching assistants: Also known as chatbot programs, machine learning–powered teaching assistants answer student questions and explain course content outside of class. These can auto-create, deliver, and grade assignments and exams, saving instructors’ time; they are downloadable from mobile app stores and can be accessed on personal devices.
• Student progress monitoring: These tools let instructors monitor academic progress, content mastery, and engagement. Custom alerts and reports identify at-risk learners and help instructors tailor the content or their teaching style for greater effectiveness. This capability is often included with subscriptions to adaptive learning platforms.

Survey respondents reported a 19 percent average increase in overall use of these learning technologies since the start of the COVID-19 pandemic. Technologies that enable connectivity and community building, such as social media–inspired discussion platforms and virtual study groups, saw the biggest uptick in use—49 percent—followed by group work tools, which grew by 29 percent (Exhibit 2). These technologies likely fill the void left by the lack of in-person experiences more effectively than individual-focused learning tools such as augmented reality and virtual reality (AR/VR). Classroom interaction technologies such as real-time chatting, polling, and breakout room discussions were the most widely used tools before the pandemic and remain so; 67 percent of survey respondents said they currently use these tools in the classroom.

About the research

In November 2021, McKinsey surveyed 634 faculty members and 818 students from public, private, and minority-serving colleges and universities over a ten-day period. The survey included only students and faculty who had some remote- or online-learning experience with any of the eight featured technologies. Respondents were 63 percent female, 35 percent male, and 2 percent other gender identities; 69 percent White, 18 percent Black or African American, 8 percent Asian, and 4 percent other ethnicities; and represented every US region. The survey asked respondents about their:

• experiences with technology in the classroom pre-COVID-19;
• experiences with technology in the classroom since the start of the COVID-19 pandemic; and
• desire for future learning experiences in relation to technology.

The shift to more interactive and diverse learning models will likely continue. One industry expert told us, “The pandemic pushed the need for a new learning experience online. It recentered institutions to think about how they’ll teach moving forward and has brought synchronous and hybrid learning into focus.” Consequently, many US colleges and universities are actively investing to scale up their online and hybrid program offerings .

Differences in adoption by type of institution observed in the research

• Historically Black colleges and universities (HBCUs) and tribal colleges and universities made the most use of classroom interactions and group work tools (55 percent) and the least use of tools for monitoring student progress (15 percent).
• Private institutions used classroom interaction technologies (84 percent) more than public institutions (63 percent).
• Public institutions, often associated with larger student populations and course sizes, employed group work and connectivity and community-building tools more often than private institutions.
• The use of AI teaching-assistant technologies increased significantly more at public institutions (30 percent) than at private institutions (9 percent), though overall usage remained comparatively higher at private institutions.
• The use of tools for monitoring student progress increased by 14 percent at private institutions, versus no growth at public institutions.

Some technologies lag behind in adoption. Tools enabling student progress monitoring, AR/VR, machine learning–powered teaching assistants (TAs), AI adaptive course delivery, and classroom exercises are currently used by less than half of survey respondents. Anecdotal evidence suggests that technologies such as AR/VR require a substantial investment in equipment and may be difficult to use at scale in classes with high enrollment. Our survey also revealed utilization disparities based on size. Small public institutions use machine learning–powered TAs, AR/VR, and technologies for monitoring student progress at double or more the rates of medium and large public institutions, perhaps because smaller, specialized schools can make more targeted and cost-effective investments. We also found that medium and large public institutions made greater use of connectivity and community-building tools than small public institutions (57 to 59 percent compared with 45 percent, respectively). Although the uptake of AI-powered tools was slower, higher-education experts we interviewed predict their use will increase; they allow faculty to tailor courses to each student’s progress, reduce their workload, and improve student engagement at scale (see sidebar “Differences in adoption by type of institution observed in the research”).

While many colleges and universities are interested in using more technologies to support student learning, the top three barriers indicated are lack of awareness, inadequate deployment capabilities, and cost (Exhibit 3).

Students want entertaining and efficient tools

More than 60 percent of students said that all the classroom learning technologies they’ve used since COVID-19 began had improved their learning and grades (Exhibit 4). However, two technologies earned higher marks than the rest for boosting academic performance: 80 percent of students cited classroom exercises, and 71 percent cited machine learning–powered teaching assistants.

Although AR/VR is not yet widely used, 37 percent of students said they are “most excited” about its potential in the classroom. While 88 percent of students believe AR/VR will make learning more entertaining, just 5 percent said they think it will improve their ability to learn or master content (Exhibit 5). Industry experts confirmed that while there is significant enthusiasm for AR/VR, its ability to improve learning outcomes is uncertain. Some data look promising. For example, in a recent pilot study, 1 “Immersive biology in the Alien Zoo: A Dreamscape Learn software product,” Dreamscape Learn, accessed October 2021. students who used a VR tool to complete coursework for an introductory biology class improved their subject mastery by an average of two letter grades.

Faculty embrace new tools but would benefit from more technical support and training

Faculty gave learning tools even higher marks than students did, for ease of use, engagement, access to course resources, and instructor connectivity. They also expressed greater excitement than students did for the future use of technologies. For example, while more than 30 percent of students expressed excitement for AR/VR and classroom interactions, more than 60 percent of faculty were excited about those, as well as machine learning–powered teaching assistants and AI adaptive technology.

Eighty-one percent or more of faculty said they feel the eight learning technology tools are a good investment of time and effort relative to the value they provide (Exhibit 6). Expert interviews suggest that employing learning technologies can be a strain on faculty members, but those we surveyed said this strain is worthwhile.

While faculty surveyed were enthusiastic about new technologies, experts we interviewed stressed some underlying challenges. For example, digital-literacy gaps have been more pronounced since the pandemic because it forced the near-universal adoption of some technology solutions, deepening a divide that was unnoticed when adoption was sporadic. More tech-savvy instructors are comfortable with interaction-engagement-focused solutions, while staff who are less familiar with these tools prefer content display and delivery-focused technologies.

According to experts we interviewed, learning new tools and features can bring on general fatigue. An associate vice president of e-learning at one university told us that faculty there found designing and executing a pilot study of VR for a computer science class difficult. “It’s a completely new way of instruction. . . . I imagine that the faculty using it now will not use it again in the spring.” Technical support and training help. A chief academic officer of e-learning who oversaw the introduction of virtual simulations for nursing and radiography students said that faculty holdouts were permitted to opt out but not to delay the program. “We structured it in a ‘we’re doing this together’ way. People who didn’t want to do it left, but we got a lot of support from vendors and training, which made it easy to implement simulations.”

Reimagining higher education in the United States

Takeaways from our research.

Despite the growing pains of digitizing the classroom learning experience, faculty and students believe there is a lot more they can gain. Faculty members are optimistic about the benefits, and students expect learning to stay entertaining and efficient. While adoption levels saw double-digit growth during the pandemic, many classrooms have yet to experience all the technologies. For institutions considering the investment, or those that have already started, there are several takeaways to keep in mind.

• It’s important for administration leaders, IT, and faculty to agree on what they want to accomplish by using a particular learning technology. Case studies and expert interviews suggest institutions that seek alignment from all their stakeholders before implementing new technologies are more successful. Is the primary objective student engagement and motivation? Better academic performance? Faculty satisfaction and retention? Once objectives are set, IT staff and faculty can collaborate more effectively in choosing the best technology and initiating programs.
• Factor in student access to technology before deployment. As education technology use grows, the digital divide for students puts access to education at risk. While all the institution types we surveyed use learning technologies in the classroom, they do so to varying degrees. For example, 55 percent of respondents from historically Black colleges and universities and tribal colleges and universities use classroom interaction tools. This is lower than public institutions’ overall utilization rate of 64 percent and private institutions’ utilization rate of 84 percent. Similarly, 15 percent of respondents from historically Black colleges and universities and tribal colleges and universities use tools for monitoring student progress, while the overall utilization rate for both public and private institutions is 25 percent.
• High-quality support eases adoption for students and faculty. Institutions that have successfully deployed new learning technologies provided technical support and training for students and guidance for faculty on how to adapt their course content and delivery. For example, institutions could include self-service resources, standardize tools for adoption, or provide stipend opportunities for faculty who attend technical training courses. One chief academic officer told us, “The adoption of platforms at the individual faculty level can be very difficult. Ease of use is still very dependent upon your IT support representative and how they will go to bat to support you.”
• Agree on impact metrics and start measuring in advance of deployment. Higher-education institutions often don’t have the means to measure the impact of their investment in learning technologies, yet it’s essential for maximizing returns. Attributing student outcomes to a specific technology can be complex due to the number of variables involved in academic performance. However, prior to investing in learning technologies, the institution and its faculty members can align on a core set of metrics to quantify and measure their impact. One approach is to measure a broad set of success indicators, such as tool usage, user satisfaction, letter grades, and DFW rates (the percentage of students who receive a D, F, or Withdraw) each term. The success indicators can then be correlated by modality—online versus hybrid versus in-class—to determine the impact of specific tools. Some universities have offered faculty grants of up to $20,000 for running pilot programs that assess whether tools are achieving high-priority objectives. “If implemented properly, at the right place, and with the right buy-in, education technology solutions are absolutely valuable and have a clear ROI,” a senior vice president of academic affairs and chief technology officer told us.

In an earlier article , we looked at the broader changes in higher education that have been prompted by the pandemic. But perhaps none has advanced as quickly as the adoption of digital learning tools. Faculty and students see substantial benefits, and adoption rates are a long way from saturation, so we can expect uptake to continue. Institutions that want to know how they stand in learning tech adoption can measure their rates and benchmark them against the averages in this article and use those comparisons to help them decide where they want to catch up or get ahead.

Claudio Brasca is a partner in McKinsey’s Bay Area office, where Varun Marya is a senior partner; Charag Krishnan is a partner in the New Jersey office; Katie Owen is an associate partner in the St. Louis office, where Joshua Sirois is a consultant; and Shyla Ziade is a consultant in the Denver office.

The authors wish to thank Paul Kim, chief technology officer and associate dean at Stanford School of Education, and Ryan Golden for their contributions to this article.

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How technology is reinventing education.

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of  Stanford Graduate School of Education  (GSE), who is also a professor of educational technology at the GSE and faculty director of the  Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately  worried  that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or  coach  students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the  AI + Education initiative  at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of  CRAFT  (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the  Digital Learning initiative  at the Stanford Accelerator for Learning, which runs a program exploring the use of  virtual field trips  to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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What’s Next for Ed Tech in 2024

Coming soon: advances in VR and high-tech classrooms, plus even more AI.

By  Lauren Coffey

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Artificial intelligence, virtual reality and more-connected classrooms could all become more prevalent in 2024.

After a 2023 filled with “ metaversities ,” robots rolling across campuses and artificial intelligence tools spurring both anxiety and excitement , what could this coming year have in store for higher ed technology? Inside Higher Ed spoke with experts who predicted what might be next for classrooms and institutions in the months ahead.

AI for Teaching

The star of the show is, unsurprisingly, AI.

“It’s the way of the future, and it’s almost like, ‘Get out of the way or get run over,’” said Marco Johnson, president and CEO of Accreditation Advisors Group. “And if you’re not jumping on the train, you better hop off, because you can’t stop it.”

After going mainstream following the launch of OpenAI’s ChatGPT in November 2022, AI started appearing in many classrooms last spring. Adoption continued in last fall, with college students across the nation turning to AI tools. Meanwhile, institutions began using AI for enterprise functions, such as streamlining admissions and financial aid processes and creating courses on ChatGPT .

Next up could be greater adoption of AI as a teaching tool.

“AI has been heavy on operational efficiency,” said Hernan Londono, chief technology and innovation strategist for education at Dell Technologies. He pointed to recruitment, admissions, retention and fundraising. “Everyone under the sun is using AI, but not for teaching, so that will start to come.”

Professors could begin using it for personalization, according to Kadriye Ercikan, vice president of the research and measurement sciences area at Educational Testing Services.

“We’re in the early stages of creating a new paradigm for personalized assessment and learning; it’s critical for moving the field forward,” she said. “It’s supporting teachers in the classroom to personalize their teaching by using AI to provide feedback for individual learners and pointing in the direction where students can go.”

AI adoption also could spell a win—or a loss—for admissions, said Johnson and Roy Mathew, who leads higher education consulting at Deloitte.

“This generation of students is more apt to look into this, and then you have the institutions saying, ‘We’re not bringing in AI,’” Johnson said. “It may turn off some students. Educators are data-driven people; it’s always, ‘Let’s see some studies.’ But you wait too long, you fall behind.”

“I think there are going to be folks who have already embraced it, [those who] are well on their way and others will be reluctantly dragged along,” Mathew said. “If [students] don’t get to experience this in the curriculum, five years from now it impacts their career prospects. So, they will speak with their feet.”

Exploring the ethics of AI in education—conversations that are just beginning on many campuses—will also play an important role in 2024.

“There will be a dominance of headlines about AI, not because we have important things to say about AI, but we have incredible anxiety about AI,” said Bridget Burns, CEO of the University Innovation Alliance.

Londono expects there to be a focus on AI privacy and fairness issues. And Ercikan added that it’s “critical” for tech companies to address ethical issues, such as data access and how AI is developed.

“How algorithms are developed and whether they service individuals from different backgrounds is really critical to look into as we think of the future of education,” she said.

Virtual Reality Takes Off

Even as AI shook many aspects of higher ed last year, other technologies were creating entirely different realities to support teaching and learning.

Arizona State University set the bar in 2023 with the use of virtual reality, work that could spread to other campuses in 2024, Burns said.

“They have reimagined science, period,” Burns said, referencing the use of immersive virtual reality in introductory biology labs. “We’re using more innovation in the classroom than ever, and the appetite to dabble and try it is greater than before.”

“I’ve been blown away by students’ reactions,” said Annie Hale, executive director of the Action Lab at Arizona State University.

Dreamscape Immersive | ASU News

Dell’s Londono pointed toward the new Quest 3 headset from Meta, the parent company of Facebook, as an example of extended and virtual reality technology becoming more accessible to classrooms.

“Anything related to extended and virtual reality tech is developing a bit more,” he said. “The fidelity of the experience is a lot higher. Every time it happens, you think of its impact on instruction [in the classroom].”

Deloitte’s Mathew agreed that VR is a technology to watch in 2024.

“There’s a wave embracing all the technologies to take the student experience to another level,” he said. “And with VR, it is so much easier to show them how to do it versus reading out of a textbook.”

Classrooms Go High-Tech

For education grounded in the real world, hybrid learning has moved from a novel concept to the norm at many institutions. This coming year will likely see more work improving classrooms to support hybrid learning.

“Since the pandemic, there are usually two sets of transformations happening,” Deloitte’s Mathew said. “Some schools are still doing nothing and continuing on with the status quo of traditional classrooms, but there’s a whole bunch of universities transforming their classrooms to enable hybrid modality.”

He mentioned the wave of institutions upgrading for hybrid and HyFlex environments, going beyond just recording a lecture and placing it online or live-streaming from the classroom.

The new digitally focused classrooms can feature upgraded projectors, which can display Zoom chats on screens so students both in and out of the classroom can interact. These classrooms also may contain more video screens, to create an immersive online experience along with having teacher assistants in the classroom answering online students’ questions.

Some classrooms may be upping their technology to make hybrid and virtual learning more high-quality.

Getty Images 

New kinds of internet-connected sensors will also be added in classrooms, according to Dell’s Londono. The sensors, part of the technology known as the internet of things, or IoT, share data in real time. They have a slew of purposes, including tracking student attendance, turning off lights when classrooms are empty, controlling temperature and monitoring devices like laptops and tablets.

“It’ll be part of the technology that happens next year,” Londono said. “5G has taken a few years to make it into higher education and been kind of dormant. Now that there’s more sensors, a focus will probably come back, especially at bigger research institutions.”

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REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

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The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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Higher Ed Institutions Adopt Modern IT Strategies to Keep Up with Technology’s Evolution

Ryan Petersen has worked in publishing for more than two decades, most of it spent creating award-winning content and strategy for CDW’s family of tech magazine brands. As editor-in-chief, he works with his team to develop compelling and useful industry-focused stories to share with the technology world. Outside of work, Ryan enjoys spending time with his children, traveling, online gaming, and following Iowa Hawkeye sports and Cubs baseball.

Technology has always evolved quickly, but we’ve seen a rapid escalation in that evolution in the past few years as tools such as artificial intelligence and Internet of Things (IoT) devices continue to gain traction across industries. In higher education, many of these tools are not only useful in the classroom and the back office, they’re also technologies that students will see in the workforce .

If higher education institutions are going to prepare the next generation of technical talent for the future, they must be staffed with experts who understand the technology themselves. They must also have the infrastructure to support these in-demand technical initiatives.

Click the banner to learn how one college evolved to meet the demands of local employers.

Emerging Technologies Require a Modern Technical Environment

Colleges and universities increasingly are adding high-tech tools to their technology inventories. Quantum computing is one field that is growing rapidly, and research institutions are taking note. Rensselaer Polytechnic Institute is the first higher ed institution to house IBM ’s quantum computer, making this type of research available to students and faculty alike. To support the high-performance computer, the school bolstered its uninterruptable power supply and broadband network.

Smart buildings are also taking hold in higher education, with institutions leaning into IoT technologies that monitor and optimize energy and water consumption to save money and environmental resources . At Case Western Reserve University , data from these sustainable buildings is used as a teaching tool for students. “This is real stuff, and it’s messy and noisy, so it has all of the things that they need to understand when they move from a classroom or laboratory into the real world,” says Professor Emeritus Kenneth Loparo.

All of these technologies require adequate resources, which is why a modern technical backbone is essential. More smart technologies mean more attack surfaces for cybercriminals, so a cyber resilience strategy is key to not only defending against potential attacks but also responding to inevitable security breaches . Modern networking is also essential to meet the added bandwidth demands of this additional technology, which is why schools such as Oral Roberts University are upgrading to Wi-Fi 6E to boost connectivity across campus .

As this evolution continues, preparing technology environments for today’s needs as well as for future expansion will be vital for institutions to continue to innovate.

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Leading the way in education technology and higher education

In 2019, the journal enjoyed significant growth and development.

Our community of authors, readers and reviewers continues to grow year on year, and we have achieved a comprehensive level of internationalization, receiving papers from all around the world. A quick breakdown of our numbers by geography.

• Last year the journal published articles from  authors representing 25 different countries; the United States, Spain, United Kingdom, Germany, Portugal, Turkey, South Africa, Australia, Belgium, China, Italy, Japan, United Arab Emirates, Argentina, Switzerland, Finland, Indonesia, Ireland, Israel, South Korea, Sri Lanka, Mexico, Netherlands, Saudi Arabia and Vietnam.
• The editorial board's members are based in 30 countries; Australia, Belgium, Brazil, Canada, Chile, Colombia, Croatia, Denmark, Ecuador, Egypt, Estonia, France, Germany, Greece, Ireland, Israel, Italy, Japan, Lithuania, Malaysia, Mexico, Portugal, Romania, South Africa, Spain, Sweden, The Netherlands, UK, United Arab Emirates, and the USA.
• 329 experts completed at least one review in 2019 and were located in 69 different countries.

We are very proud of  this achievement and we would like to thank our journal's extensive community, our international advisory board and our editorial board for their outstanding support.

2019, was marked by one further significant achievement by the journal: when we received our first Journal Impact Factor (1.922), corresponding to 2018.  This is further compounded with the substantial improvement of the journal in other impact indexes such as; CiteScore, Google Scholar Citations, SJR and SNIP. The journal  has also  improved in altmetrics, which measure the journal's social impact. We continue to work on maintaining and improving the services we offer our authors, guaranteeing short review periods between the submission and publication of their papers. This has been possible thanks to the valuable support provided by our team of reviewers (numbering 329 in 2019), who carry out the review process diligently and with academic rigour, and to the journal's editorial management team. Our social media presence continues to grow, especially on Twitter, where we now have 3,180 followers.

Two thematic issues were published in 2019, “ Technology Enhanced Learning or Learning Driven by Technology ? ” and “ Food, Nutrition and the Online: Opportunities and Challenges for Higher Education and Lifelong learning ”. The latter was coordinated by two experts in Health Sciences from the UOC, F. Xavier Medina and Alicia Aguilar.

The beginning of 2020 will see the publication of articles from our latest thematic series , which explores the potential of artificial intelligence in education. The series was coordinated by international experts including; Tony Bates (Ryerson University), Cristóbal Cobo, (Center for Research Ceibal Foundation), Olga Mariño (University of Los Andes) and Steve Wheeler (Plymouth Institute of Education). Currently the first two articles are available to our readership: an analysis of educational data to predict academic performance and a review of the research into the application of artificial intelligence in higher education. Furthermore, in March a new series will be published; Towards a critical perspective on data literacy in higher education. Emerging practices and challenges. Edited by: Juliana Elisa Raffaghelli, Stefania Manca, Bonnie Stewart, Paul Prinsloo and Albert Sangrà.

Despite all the major achievements of the journal, many challenges and opportunities exist for researchers and practitioners in the area of educational technology in higher education. Changes in teaching and learning, organizational dynamics of higher education institutions, the need for research to provide evidence and results of application of technologies in education to support continuous improvement and progress of higher education are as urgent, as ever. This is why our journal is a focal point of scholarship and support to HE communities, serving with open access publication of quality research, the International Journal of Educational Technology in Higher Education (ETHE) is an important source of research and point of contact for the academic community immersed in this field. ETHE brings academic rigour and insight to the field of education technology by enabling the publication and dissemination of international research in the field.

The journal’s position in the field continues to improve and we will continue to work progressively to maintain and enhance its position as a leading international journal for education and technology in higher education. Our goal for 2020 is to ensure that the journal is recognised as the best platform for publishing and disseminating research related to educational technology in higher education.

This goal is underpinned by the following three principles, where we 

• Ensure the high quality of the articles we publish, thanks to the excellent work of our community of reviewers, with whom we will continue to work and to improve our internal review process.
• Focus on international research relating to current and innovative issues of debate and reflection that impact in the field of educational technology in higher education, involving a rigorous selection from papers received, and by publishing bespoke collections by prestigious guest editors.
• Disseminate the knowledge we publish openly, extensively and on a mass scale through our journal's open pages and social media, thanks to the support of the four institutions that fund the journal.

Our international advisory board, whose members are experts from every continent, works constantly to improve the quality processes that maintain and enhance our journal's academic standing. Coordinating this is our team of editors, derived from the four universities that fund the journal.

We wish you the best for 2020. We hope that you will continue to engage with the International Journal of Educational Technology in Higher Education and keep up-to-date with the latest research in the field of technology in higher education .  We will continue to publish research and collections of the highest quality and we cordially invite you to submit your work and findings for review with us.

Kind regards,

Josep M. Duart , Universitat Oberta de Catalunya, Spain  (Editor-in-Chief)

Álvaro Galvis , Universidad de los Andes, Colombia (Editor-in-Chief)

Mairéad Nic Giolla Mhichíl , Dublin City University, Ireland (Editor-in-Chief)

Airina Volungevičienė , Vytautas Magnus University, Lithuania (Editor-in-Chief)

Elsa Corominas , Universitat Oberta de Catalunya, Spain (Managing Editor)

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7 Research Findings About Technology and Education

Here’s what research shows about the effectiveness of technology for learning and when less tech can be more productive.

Do students perform better on digital or paper assessments? Does the amount of time spent on an app correlate to learning growth? How much valid and reliable research is typically behind an educational application? These are questions that busy educators often wonder about, yet they may not have an easy way to find answers. Fortunately, there is research on education apps and devices as well as learning growth and outcomes in the research journals. Below are seven things that educators should know about the research on the effectiveness of technology for learning—note that research findings can evolve over time, and the points below are not definitively settled.

Advantages and disadvantages of Tech

1. When screens are present but not being used for learning, students tend to learn less. Whether it’s a laptop or a smartphone , studies have found that the mere presence of these devices reduces available cognitive capacity in college students. Long-term recall and retention of information decreases when students at the university level have screens present during direct instructional time . Just having a laptop screen open or a cell phone next to a student (but not being used) is enough to distract their brain from fully focusing on the class activities.

Further, studies found that students in college who send off-task text or IM messages during class or engage with social media on their devices typically take lower-quality notes, and their overall academic performance is worse than that of those who didn’t engage in those activities during class. It’s important to note that when a student doesn’t have a device but is near another student who is using a device during class, both students’ grades will likely be negatively affected.

2. Literacy applications often have little valid and reliable research associated with them. A number of applications in the app stores (such as Google Play) do not have much, if any, valid and reliable research associated with them. According to a study looking at the top-rated early literacy applications , 77 percent of the applications have zero reliable research behind them. And the few apps that did have research only considered the look and feel of the application (such as ease of navigation or visual appeal), rather than if the child was likely to learn foundational literacy skills from the app.

There are apps that are effective , but finding them in the sea of all available apps—many of them poorly designed, with inadequate backing evidence—is a daunting task.

3. Neither the amount of time spent on an app nor the number of sessions in an app correlates with effectiveness. A recent study found that the “dosage” of the app, such as the number of sessions, time spent per session, and duration of the study, did not predict effectiveness of the app . Thus, learning outcomes did not change if a student spent more or less time in an application. The quality of the application matters more in determining learning growth or outcomes than the amount of time or number of times an application is used.

4. Students who read online tend to comprehend less than those who read via paper. Studies have shown that when it comes to comprehension and reading online versus on paper , the type of text matters. One study discovered that when it comes to leisure reading , the more complex the text, the more likely students will comprehend the content better when reading on paper.

Print reading over a long period of time could boost comprehension skills by six to eight times more than digital reading. The same study found that younger children (ages 6–12) seem to benefit the most from print reading over online. Further, another recent study found that university students tend to annotate more when reading on paper versus digital text, though this does not improve their subsequent memory of the text.

5. Students tend to perform worse when testing online compared with those who test on paper. While many standardized tests have moved online, there’s research that doesn’t support this as the best medium for optimal outcomes. A 2018 study determined that students tend to score worse when testing online versus paper in both math and English language arts. In particular, English language learners, children from lower-income homes, and students on individualized education programs perform worse online than on paper.

Some studies are finding that the use of computers in formal assessments creates an obstacle for students who need special accommodations like text-to-speech readers or language translators. For example, students with visual impairments tended to perform worse on computer-based tests that provided a digital reader, compared with similar students who took paper tests with a human reader.

6. Online classes are best for students who can self-regulate and are independent learners. The Brookings Institution’s Executive Summary on online learning finds that online learning is best suited for students who are high achievers and self-motivated. The research they reviewed found that academically strong students can benefit from fully online courses, while students who are not academically strong tend to do worse in online courses than they would in in-person classes.

One example is the Back on Track study, which looked at ninth-grade students taking credit recovery algebra. The study compared students in a fully online algebra credit recovery course with students in an in-person credit recovery algebra course; the fully online students had worse overall academic outcomes and were less likely to recover credit. Additionally, students in fully online courses with no face-to-face instructor interaction typically fared worse than students in face-to-face classes. The good news is that students in blended courses (part online and part in-person) appear to do about the same as those in fully in-person classes.

7. The type of device matters. While schools often shop for the least expensive option for student devices, it is important to note that a recent study looking at remote learning found that the type and quality of student devices matters in learning outcomes. Students who used devices that were older and had slower processors had a worse quality of learning experiences than those who had newer devices with stronger specifications.

These are some highlights from recent studies that can inform teachers and school districts when it comes to decision-making with purchasing technology, creating policies, or devising alternative academic offerings. It is important to understand the evidence behind any edtech-related decisions that could impact many students.

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Academics blame technology for increased burnout

Only half of us faculty members surveyed believe ai will enhance the student experience, as many report being left out of decisions on how technology is used.

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Almost half of faculty members across the US feel burned out because of their work, and a similar proportion (39 per cent) feel emotionally exhausted, according to a  report released by WGU Labs.

While burnout among academics is nothing new, Omid Fotuhi, director of learning innovation at WGU Labs – a subset of Western Governors University – believes technology could be greatly contributing to the problem. According to the report, roughly eight in 10 faculty members feel that they are always “on the job” because of technology, while 64 per cent say technology makes it difficult to take breaks from students or work.

“Faculty now see technology as not only a permanent but also a growing influence on learning,” write the report’s authors, noting that that viewpoint can create a divide between professors who want technology in the classroom and those who do not. “Such growing chasms among faculty may pose challenges, inefficiencies, and inconsistencies in teaching and instruction, which administrators must navigate.”

This is WGU Labs’ third annual report focused on faculty. The network, a consortium that supports higher education institutions as they navigate emerging technology, also releases annual reports on administrators’ and students’ thoughts about tech and innovation in the classroom.

“We’ve been tracking perceptions, beliefs, behaviours and anticipating investment in education from faculty, students and administration, with the intention to connect the dots,” Dr Fotuhi said. “There were a lot of questions we couldn’t answer by talking to just one group.”

The faculty report surveyed 359 faculty members in November 2023, spanning community colleges, online-only colleges and bricks-and-mortar, four-year institutions.

The intersection of the reports over the years has allowed Dr Fotuhi to delve further into the latest findings. He said he has always been struck by faculty members’ ambivalence towards artificial intelligence, with many using the tools but retaining scepticism about its efficacy. According to the report, more than half (53 per cent) of instructors believe AI will enhance the student experience – although a similar percentage are not using it in their classrooms. That lines up with other reports that find that students’ AI usage far outpaces faculty use.

The scepticism may derive partly from how edtech decisions are being made. According to the latest report, 87 per cent of faculty members said their administrative team makes decisions on edtech implementation and usage. Less than 20 per cent of academics reported that their institutions sought their feedback on edtech at least once a year, and about the same percentage said their institutions involved students in the process.

“That’s where we got to the root cause: Faculty don’t feel they’re involved in the decision-making process,” Dr Fotuhi said. “They don’t think their input is valued, which fuels the idea about [technology’s] effectiveness.”

Those feelings about technology’s effectiveness play into faculty members’ thoughts about the direction of higher education overall. They acknowledge reality: according to the report, almost all faculty members (92 per cent) believe they will use more edtech tools, such as AI, in the future. The vast majority (86 per cent) also expect to spend more time delivering course content online. But 20 per cent believe higher education is heading in the wrong direction because of its focus on technology in the classroom, with just 32 per cent believing it is going in the right direction.

More worryingly, one-third (37 per cent) of faculty members said students would have lower-quality learning experiences in the future because of the increasing use of technology. A similar percentage believe that the value of higher education will decline going forward.

Perhaps unsurprisingly, that outlook shifts a bit when accounting for the type of instructors being surveyed: roughly 40 per cent of those who teach online believe that higher education is headed in the right direction, in part because of its increased use of technology, while just 20 per cent of professors teaching in-person courses state the same.

Despite the difference in future outlooks, more professors are taking a positive view towards course modalities than in past reports. Seventy-nine per cent of faculty members said they feel positive about offering more modality and credential options to students, while 76 per cent feel positive about offering more hybrid courses (mixing remote and in-person instruction) for students. By contrast, the 2023 report found that just over half of faculty felt positively about institutions offering increasing numbers of online courses and programmes.

Dr Fotuhi suggested that institutions do two things to combat the rising tensions surrounding technology and burnout. First, when an institution is considering technology investments, it should offer channels for academics and students to voice their opinions on potential changes. And then, when those investments are made, the university should offer support and guidelines to implement the new infrastructure or technology.

That it is easier said than done, Dr Fotuhi acknowledged. “Most administrators, they’re just fighting to stay afloat; it’s a really difficult time for higher education. Administrators are making decisions on the limited information they have; that affects faculty on support and job satisfaction, which impacts students. It’s a systems issue, so we’re trying to connect the dots.”

This is an edited version of a story that first appeared on Inside Higher Ed .

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An investigative study among higher education students’ continuance intention towards e-learning in India

• Published: 07 September 2024

Cite this article

• Anamika Chandra 1 ,
• Sarthak Sengupta   ORCID: orcid.org/0000-0001-8179-2457 2 &
• Anurika Vaish 3  

E-learning systems have strengthened the learning process among students by providing online learning opportunities to them. With the diminishing difference between the academic world and the job markets, students must get the full benefits of e-learning platforms. The disparity in online learning opportunities due to a lack of resources has pushed students in developing countries to a situation where it has been difficult for them to continue through online learning for self-development. E-learning practices in developing countries may contribute to achieving sustainable development of equity in quality education. E-learning systems are increasingly used in India but students’ readiness to adopt them to connect globally depends on several factors. The use of online systems of study was reinforced during the COVID-19 pandemic in India, similar to other countries, due to the closure of schools and colleges. This study is conducted to analyze students’ continuance intention toward e-learning after the COVID-19 pandemic using the extended Unified Theory of Acceptance and Use of Technology model (UTAUT). Students’ responses, those studying in under-graduation and post-graduation courses were collected through questionnaires. Data analysis was done by Structural Equation Modeling (SEM). Most of the respondents had perceived e-learning positively, anticipated applying it in their learning process, and felt fairly competent while using it. A 55.4% variance in Continuance Intention was found directly due to other variables determining the behavioral aspects of students toward e-learning. When students feel e-learning is engaging and consider it easy to use and supportive in improving their effectiveness and learning performance, they feel satisfied using it and their continuance intention for using it is enhanced. International academic institutions and schools across the world should promote and incorporate e-learning systems as part of teaching pedagogy to empower students with adequate skills for e-learning.

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Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies , 26 (6), 7205–7224.

Article   Google Scholar  

Akbari, M., Danesh, M., Moumenihelali, H., et al. (2022). How does Identity Theory contribute to the Continuance Use of E-learning: The mediating role of Inertia and moderating role of computer self-efficacy . Education and Information Technologies.

Akdim, K., Casaló, L. V., & Flavián, C. (2022). The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. Journal of Retailing and Consumer Services , 66 , 102888.

Al-Adwan, A. S., Yaseen, H., Alsoud, A., Abousweilem, F., & Al-Rahmi, W. M. (2022). Novel extension of the UTAUT model to understand continued usage intention of learning management systems: The role of learning tradition. Education and Information Technologies , 27 (3), 3567–3593.

Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics , 12 (1), 27–50.

Article   MathSciNet   Google Scholar  

Al-Qeisi, K. I. (2009). Analyzing the use of UTAUT model in explaining an online behaviour: Internet banking adoption (Doctoral dissertation, Brunel University Brunel Business School PhD Theses).

Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. Ieee Access , 7 , 26797–26809.

Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Alyoussef, I. Y., Al-Rahmi, A. M., & Kamin, Y. B. (2021). Integrating innovation diffusion theory with technology acceptance model: Supporting students’ attitude towards using a massive open online course (MOOCs) systems. Interactive Learning Environments , 29 (8), 1380–1392.

Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: A unified perspective from instructors and students. Studies in Higher Education , 43 (11), 2003–2019.

Alam, M. J., Ogawa, K., & Islam, S. R. B. (2023). e-Learning as a doubled-Edge Sword for Academic achievements of University students in developing countries: Insights from Bangladesh. Sustainability , 15 (9), 7282.

Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: The modified UTAUT model for Saudi Arabia. Education and Information Technologies , 26 , 1589–1605.

Amoroso, D. L., & Chen, Y. A. (2017). Constructs affecting continuance intention in consumers with mobile financial apps: A dual factor approach. Journal of Information Technology Management , 28 No. 3.

Bandura, A. (1986). Social foundations of Thought and Action: A Social Cognitive Theory . Prentice-Hall.

Banu, R., Shrivastava, P., & Salman, M. (2024). An empirical study of students’ perceptive on e-learning systems success. The International Journal of Information and Learning Technology .

Batucan, G. B., Gonzales, G. G., Balbuena, M. G., Pasaol, K. R. B., Seno, D. N., & Gonzales, R. R. (2022). An extended UTAUT model to explain factors affecting online learning system amidst COVID-19 pandemic: The case of a developing economy. Frontiers in Artificial Intelligence , 5 , 84.

Blut, M., & Wang, C. (2020). Technology readiness: A meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science , 48 (4), 649–669.

Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. Journal of Computing in Higher Education , 34 (3), 844–867.

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology , 10 , 1652.

Chauhan, S., Goyal, S., Bhardwaj, A. K., & Sergi, B. S. (2022). Examining continuance intention in business schools with digital classroom methods during COVID-19: A comparative study of India and Italy. Behaviour & Information Technology , 41 (8), 1596–1619.

Chen, P. Y., & Hwang, G. J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education , 39 (1), 79–95.

Chen, M., Wang, X., Wang, J., Zuo, C., Tian, J., & Cui, Y. (2021). Factors affecting college students’ continuous intention to use online course platform. SN Computer Science , 2 (2), 1–11.

Cheng, Y. M. (2020). Students’ satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education + Training .

Chow, W. S., & Shi, S. (2014). Investigating students’ satisfaction and continuance intention toward e-learning: An extension of the expectation–confirmation model. Procedia-Social and Behavioral Sciences , 141 , 1145–1149.

Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education , 122 , 273–290.

Cimperman, M., Brenci ˇ c, M. M., & Trkman, P. (2016). Analyzing older ˇ users’ home telehealth services acceptance behavior—applying an extended UTAUT model. Int J Med Inform , 90 , 22–31.

Dakduk, S., Santalla-Banderali, Z., & Van Der Woude, D. (2018). Acceptance of blended learning in executive education. Sage Open , 8 (3), 2158244018800647.

Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning , 11 (2), 201–214.

Google Scholar  

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science , 35 (8), 982–1003.

Du, W., & Liang, R. Y. (2024). Teachers’ continued VR Technology usage intention: An application of the UTAUT2 model. SAGE Open , 14 (1), 21582440231220112.

Duggal, S. (2022). Factors impacting acceptance of e-learning in India: learners’ perspective. Asian Association of Open Universities Journal , (ahead-of-print).

Edem Adzovie, D., & Jibril, A. B. (2022). Assessment of the effects of Covid-19 pandemic on the prospects of e-learning in higher learning institutions: The mediating role of academic innovativeness and technological growth. Cogent Education , 9 (1), 2041222.

Enrique Hinostroza, J. (2018). New challenges for ICT in education policies in developing countries: The need to account for the widespread use of ICT for teaching and learning outside the school. ICT-Supported innovations in small countries and developing regions (pp. 99–119). Springer.

Esawe, A. T., Esawe, K. T., & Esawe, N. T. (2022). Acceptance of the learning management system in the time of COVID-19 pandemic: An application and extension of the unified theory of acceptance and use of technology model. E-Learning and Digital Media , 20427530221107788.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research , 18 (1), 39–50.

Gaftandzhieva, S., Talukder, A., Gohain, N., Hussain, S., Theodorou, P., Salal, Y. K., & Doneva, R. (2022). Exploring online activities to predict the final grade of student. Mathematics , 10 (20), 3758.

Gurban, M. A., & Almogren, A. S. (2022). Students’ actual use of E-Learning in Higher Education during the COVID-19 pandemic. SAGE Open , 12 (2), 21582440221091250.

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the technology acceptance model for use of e-learning systems by digital learners. Ieee Access , 6 , 73395–73404.

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics , 101 , 75–84.

Howard, M. C. (2014). Creation of a computer self-efficacy measure: Analysis of internal consistency, psychometric properties, and validity. Cyberpsychology Behavior and Social Networking , 17 (10), 677–681.

Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies , 25 (5), 4615–4635.

Huang, F., Teo, T., & Scherer, R. (2022). Investigating the antecedents of university students’ perceived ease of using the internet for learning. Interactive Learning Environments , 30 (6), 1060–1076.

Ifinedo, P. (2018). Roles of perceived fit and perceived individual learning support in students’ weblogs continuance usage intention. International Journal of Educational Technology in Higher Education , 15 (1), 1–18.

Isaac, O., Abdullah, Z., Aldholay, A. H., & Ameen, A. A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pacific Management Review , 24 (4), 335–354.

Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior , 69 , 83–90.

Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics , 34 , 1250–1261.

Kalpande, S. D., & Toke, L. K. (2023). Reliability analysis and hypothesis testing of critical success factors of total productive maintenance. International Journal of Quality & Reliability Management , 40 (1), 238–266.

Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior , 70 , 460–474.

Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computer in Higher Education , 25 , 93–121. https://doi.org/10.1007/s12528-013-9069-3

Lin, H. M., Lee, M. H., Liang, J. C., Chang, H. Y., Huang, P., & Tsai, C. C. (2020). A review of using partial least square structural equation modeling in e-learning research. British Journal of Educational Technology , 51 (4), 1354–1372.

Lizcano, D., Lara, J. A., White, B., & Aljawarneh, S. (2020). Blockchain-based approach to create a model of trust in open and ubiquitous higher education. Journal of Computing in Higher Education , 32 (1), 109–134.

Lu, X., Wang, L., Xu, G., Teng, H., Li, J., & Guo, Y. (2023). Development and initial validation of the psychological capital scale for nurses in Chinese local context. BMC Nursing , 22 (1), 28.

Malanga, A. C. M., Bernardes, R. C., Borini, F. M., Pereira, R. M., & Rossetto, D. E. (2022). Towards integrating quality in theoretical models of acceptance: An extended proposed model applied to e-learning services. British Journal of Educational Technology , 53 (1), 8–22.

Marandu, E. E., Mathew, I. R., Svotwa, T. D., Machera, R. P., & Jaiyeoba, O. (2023). Predicting students’ intention to continue online learning post-COVID-19 pandemic: Extension of the unified theory of acceptance and usage technology. Journal of Applied Research in Higher Education , 15 (3), 681–697.

Mathrani, A., Sarvesh, T., & Umer, R. (2021). Digital divide framework: Online learning in developing countries during the COVID-19 lockdown. Globalisation Societies and Education , 1–16.

Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education , 82 , 11.

Ng, H. S., Kee, D. M. H., & Ramayah, T. (2020). Examining the mediating role of innovativeness in the link between core competencies and SME performance. Journal of Small Business and Enterprise Development , 27 (1), 103–129.

Pardamean, B., & Susanto, M. (2012). Assessing user acceptance toward blog technology using the UTAUT model. International Journal of Mathematics and Computer in Simulation , 6 , 203–212.

Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management , 54 , 102144.

Petersen, F., Jacobs, M., & Pather, S. (2020). Barriers for user acceptance of mobile health applications for diabetic patients: applying the UTAUT model. In Conference on e-Business, e-Services and e-Society (pp. 61–72). Springer, Cham.

Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies , 26 (3), 2623–2656.

Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J., & Martín-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services , 50 , 85–93.

Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research , 59 (2), 183–208.

Rekha, I. S., Shetty, J., & Basri, S. (2022). Students’ continuance intention to use MOOCs: Empirical evidence from India. Educ Inf Technol .

Roy, S. (2021). Reshaping Indian Higher Education Post COVID-19: A case for blended learning and widespread adoption of Learning Management systems. Comparative advantage in the Knowledge Economy (pp. 41–51). Emerald Publishing Limited.

Salloum, S. A., & Shaalan, K. (2018). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International conference on advanced intelligent systems and informatics (pp. 469–480). Springer, Cham.

Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access , 7 , 128445–128462.

Sharma, P. C., & Pandey, A. (2023). Significance of e-Learning in Indian Modern Higher Education System: A review. Redefining Virtual Teaching Learning Pedagogy , 97–109.

Sheeran, P., & Webb, T. L. (2016). The intention–behavior gap. Social and personality psychology compass , 10 (9), 503–518.Asare, A., Yun-Fei, S., & Adjei-Budu, K. (2016). Adoption of e-learning in higher education: Expansion of UTAUT model. European Academic Research, 3, 13236–13259.

Steyn, A. A., van Slyke, C., Dick, G., Twinomurinzi, H., & Amusa, L. B. (2024). Student intentions to continue with distance learning post-COVID: An empirical analysis. Plos One , 19(1), e0293065.

Tadesse, S., & Muluye, W. (2020). The impact of COVID-19 pandemic on education system in developing countries: A review. Open Journal of Social Sciences , 8 (10), 159–170.

Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among student teachers. Journal of Educational Computing Research , 48 (1), 71–95.

Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2021). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management .

Tzivinikou, S., Charitaki, G., & Kagkara, D. (2021). Distance Education attitudes (DEAS) during Covid-19 crisis: Factor structure, reliability and construct validity of the brief DEA scale in Greek-speaking SEND teachers. Technology Knowledge and Learning , 26 , 461–479.

Valencia-Arias, A., Chalela-Naffah, S., & Bermúdez-Hernández, J. (2019). A proposed model of e-learning tools acceptance among university students in developing countries. Education and Information Technologies , 24 (2), 1057–1071.

Vanitha, P. S., & Alathur, S. (2021). Factors influencing E-learning adoption in India: Learners’ perspective. Education and Information Technologies , 26 (5), 5199–5236.

Vasuthevan, K., Vaithilingam, S., & Ng, J. W. J. (2024). Academics’ continuance intention to use learning technologies during COVID-19 and beyond. Plos One , 19(1), e0295746.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences , 39 (2), 273–315.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478. Retrieved from http://www.jstor.org/stable/30036540

Wan, L., Xie, S., & Shu, A. (2020). Toward an understanding of university students’ continued intention to use MOOCs: When UTAUT model meets TTF model. Sage Open , 10 (3), 2158244020941858.

Wang, L., Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon , 5(6), e01788.

Wang, X. Y., Li, G., Malik, S., & Anwar, A. (2022). Impact of COVID-19 on achieving the goal of sustainable development: E-learning and educational productivity. Economic Research-Ekonomska Istraživanja , 35 (1), 1950–1966.

Wut, T. M., & Lee, S. W. (2021). Factors affecting students’ online behavioral intention in using discussion forum. Interactive Technology and Smart Education .

Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems , 44 (3), 300–318.

Yu, L., Chen, Z., Yao, P., & Liu, H. (2021). A study on the factors influencing users’ online knowledge paying-behavior based on the UTAUT model. Journal of Theoretical and Applied Electronic Commerce Research , 16 (5), 1768–1790.

Zheng, H., Qian, Y., Wang, Z., & Wu, Y. (2023). Research on the influence of E-Learning quality on the intention to continue E-Learning: Evidence from SEM and fsQCA. Sustainability , 15 (6), 5557.

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Figure: Conceptualized extended UTAUT model with path analysis: path coefficients at p < 001 (*) and p < 01 (**) (with variable codes of the constructs in the model)

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The use of technology in higher education teaching by academics during the COVID-19 emergency remote teaching period: a systematic review

Mcqueen sum.

Department of Education, University of Oxford, 15 Norham Gardens, Oxford, OX2 6PY UK

Alis Oancea

Associated data.

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This paper presents a systematic review of scholarly efforts that uniquely emerged at the onset of the COVID-19 pandemic and focused primarily on higher education teachers’ perspectives on technology use and on associated changes in the relationship between teachers and students amidst the transition to emergency remote teaching worldwide. Our narrative synthesis of 32 studies, the majority of which come from lower-and middle-income countries/regions, suggests that numerous factors interact to shape academics’ technology use in emergency remote teaching across higher education contexts. We report strong findings of teachers’ resilience and resourcefulness in their self-exploration of various technologies and teaching strategies in response to the continued severity of the pandemic. Ultimately, this review suggests directions for further research on engaging educational leaders and faculty in reimagining teaching as not only a core academic function of higher education, but also, and importantly, a humanising experience shaped by an ethics of care.

Review of literature and research questions

Since the continued devastating spread of COVID-19 across continents from early 2020, the coronavirus pandemic has led to massive numbers of hospitalisations and deaths around the world, abruptly upending public health and many other domains of life. As the disaster has unfolded, a multitude of sweeping challenges have continued to reshape the global higher education (‘HE’) landscape. With HE institutions (‘HEIs’) worldwide closing their campuses in Spring 2020, teachers were forced to make a hasty transition from typically in-person teaching configured in physically proximate space to alternative teaching approaches in response to the COVID-19 emergency (Crawford et al., 2020 ).

The term ‘emergency remote teaching’ (‘ERT’) is used by Hodges et al. ( 2020 ) and subsequent literature to denote the rapid and putatively ephemeral shift to remote teaching to continue teaching and learning during emergencies. Although ‘ERT’ and ‘online teaching’ may be two domains with considerable overlaps, ‘online teaching’ is importantly distinguished from ‘ERT’ as it includes teaching and learning arising from a prolonged collective effort in curriculum planning and instructional design from a wide range of stakeholders pre-launching (Hodges et al., 2020 ).

Despite the growing literature on ERT, few efforts had been made to review this body of research systematically at the time of conducting this review (see Table ​ Table1 1 for a few exceptions). Since there have been abundant discussions on the perspectives of students at the HE level during COVID-19 [see, for example, Chakraborty et al. ( 2021 ) on Indian students’ opinions on various aspects of ERT; Mok et al. ( 2021 ) on Hong Kong students’ evaluation of their learning experiences during ERT; Resch et al. ( 2022 ) on social and academic integration of Austrian students; and Salas-Pilco et al. ( 2022 ) for a systematic review focusing on student engagement in Latin American HE], our review focuses systematically on synthesising the body of worldwide literature on teachers’ perspectives on technology use during the period of ERT. Moreover, much attention has been devoted to medical education (Rajab et al., 2020 ; see also Table ​ Table1) 1 ) and STEM education since the coronavirus outbreak (Amunga, 2021 ; Bond et al., 2021 ; Gaur et al., 2020 ; Singh-Pillay & Naidoo, 2020 ). Our review focuses on the less explored perspectives of humanities, arts, and social sciences (HASS) teachers—whose perceived difficulties of using digital technologies in teaching were reportedly distinct from those of their counterparts in other disciplines, both before (Mercader & Gairín, 2020 ) and during the COVID-19 outbreak (Wu et al., 2020 ).

Prior systematic reviews published in 2020–2021 pertaining to technology use in higher education teaching in the COVID-19 context

Prior to COVID-19, a respectable amount of scholarly work was devoted to the development and adaptation of theoretical models to identify, explain, and even predict factors that influenced technology use in educational contexts (Granić & Marangunić, 2019 ). But Lee and Jung ( 2021 ) argue that ‘in higher education contexts, crisis-driven changes may happen differently from pre-planned, voluntary change, and that factors influencing crisis-driven changes are different from those influencing voluntary changes; as reported in previous studies based on technology acceptance theories and models’ (p. 16). Given the novelty of COVID-19, few studies have been conducted to explicate the factors shaping HE teachers’ decisions about, and experiences of, technology use in the unique context of the global pandemic [see Mittal et al. ( 2021 ) for an exception that studies faculty members in Northern India and Lee and Jung ( 2021 ) for another study on South Korean university educators]. Therefore, the first question that this review aims to answer is: How have different potential factors, as identified by teachers in the included studies, shaped teachers’ technology use across various higher education contexts during the COVID-19 emergency remote teaching period?

Existing scholarly efforts that aim to provide an overview of the literature focus predominantly on a bifurcated discussion of the opportunities and challenges, or advantages and disadvantages pertinent to using technologies in teaching during the COVID-19 crisis (Adedoyin & Soykan, 2020 ; Dhawan, 2020 ; Pokhrel & Chhetri, 2021 ; Stewart, 2021 ). We therefore frame the second research question in a way that circumvents a binary pros-and-cons discussion of the implications of technology use in times of the COVID pandemic, as already well-documented in the literature. Hence, our second question is: What are the implications of technology use in COVID-19 emergency remote teaching from the perspectives of higher education teachers?

The broader term ‘technology’ (in the singular form) used in the review questions includes the socio-cultural contexts of the educational settings in which technology use is situated. The discussion of ‘context’ is of particular importance (Selwyn, 2022 ). Although pre-COVID studies (such as Broadbent & Poon, 2015 ; Liu et al., 2020 ) offered valuable insights into technology use in HE teaching, the pandemic brought about starkly and often perilously different contexts for research as well as for teaching and learning (Stewart, 2021 ; Williamson et al., 2020 ).

We use the term ‘technologies’ in its plural form throughout this review, in a narrower sense, meaning specifically the wide range of digital tools and systems and other technical resources that are used for pedagogical purposes. These can include but are not limited to electronic hardware devices, software systems, online services, and social media. We note, however, that the meanings attached to the term ‘technologies’ may be substantively different across contexts. Some of the studies included in this review, as we will show below, extend it to other-than-digital forms of technologies, leading to results beyond our initial scope of research. As a result, the use of (digital) technologies is understood in this review as an often necessary but not sufficient condition for ERT—a novel concept to many teachers who had been using various ‘technologies’ in other ways in facilitating their teaching for years before the COVID-19 outbreak.

Methodology

Characterised by the principles of replicability and transparency, a systematic review aims to ‘review ... existing research using explicit, accountable rigorous research methods’ (Gough et al., 2017 , p. 4). This methodology is used because it helps elucidate the current understanding and available evidence of the above research questions, clarify any replication of existing research findings, and inform future research and policy directions in HE teaching in a systematic and trustworthy manner. Below is a detailed, transparent report of the processes involved in conducting this systematic review.

Inclusion/exclusion criteria

Our review is restricted to peer-reviewed journal articles that report original empirical studies written in English and/or simplified Chinese. Papers written in these two languages account for a high volume of worldwide literature published at the onset of the COVID-19 outbreak. Also, Chinese studies are particularly valuable for this review, for mainland China was the first region affected by COVID-19 and its HE system was amongst the first to respond to the challenges ensuing from the spread of coronavirus.

Since the review seeks to capture a ‘snapshot’ of perspectives on technology use by teachers during the immediate COVID-19 outbreak, only articles published in 2020 (including those published online ahead of print that year) were eligible for review. Included publications may cover any country/region worldwide but should systematically gather data from teachers other than the authors themselves and focus primarily on the perspectives of HASS teachers on matters pertaining to technology use in ERT in HE settings. Opinion pieces, editorials, reflection articles on one’s own practice, conference papers, and books are not within the purview of this review (see Appendix 1  for detailed inclusion/exclusion criteria).

Search strategy

Prior to conducting the database search, we piloted and modified the search strings several times. Our final search strategy is a combination of Boolean operators and variations of four key terms: ‘higher education’, ‘technology’, ‘teaching’, and ‘COVID-19’ (see Appendix 2  for detailed search terms).

Screening and selection

On 13 January 2021, a targeted search returned 4204 records indexed in fourteen databases including Scopus, Web of Science, and three Chinese databases (see Appendix 3  for PRISMA flow diagram and the complete list of databases). From these, we extracted 20 different papers at random to screen by title and abstract independently by applying the inclusion/exclusion criteria, and with the intention to repeat the process until unanimous agreement was reached. Having achieved full inter-reviewer agreement in our first attempt and after a further calibration session, we then proceeded to de-duplication and title-and-abstract screening, after which only 129 papers remained for full-text retrieval and further screening. Meanwhile, 16 relevant publications from various other sources were also identified and passed the initial screening. We then examined the full text of the resulting total of 145 articles and excluded any that did not fulfil the inclusion criteria, leading to a set of 40 studies to be considered for review.

Quality and relevance assessment and content extraction

To assess the 40 papers’ quality and relevance to this review, we adapted the assessment rubric from Oancea et al. ( 2021 ) (see Appendix 4 ). In parallel with the quality assessment, we developed a grid for content extraction by piloting on three papers, after which multiple revisions of the extraction grid were made. Then both authors used the updated extraction grid (see Appendix 5 ) and extracted content from two full papers independently to check for inter-reviewer agreement. In subsequent communications, discrepancies of our extraction were reconciled and the final quality thresholds for inclusion were agreed upon. As of May 2021, after excluding 8 papers of low quality, the final corpus for review comprised 32 articles.

Analysis and synthesis

We developed an initial coding scheme with broad theme boundaries based on the research questions, and resolved any conflicting views. We coded line-by-line the extracted data both deductively and inductively: we first applied the pre-configured coding scheme to the full set of data, and then updated and re-applied the coding scheme to include further themes identified through inductive coding. For example, we realised that the category of ‘ethical use of technology’ spanned the themes of ‘pedagogical implications’ and ‘work-related implications’. As a result we categorised it under a separate theme titled ‘cross-cutting implications’. After multiple rounds of scheme refinement and iterative coding which started in June 2021, the process of synthesis concluded in late December 2021.

The research synthesis is presented narratively; note that we integrated quantitative findings (for example, from surveys) descriptively into the narrative analysis, as in most cases the samples were not representative, the analysis was largely descriptive and findings from qualitative answers to open questions were presented in detail.

Limitations

Our review did not include insights from reflection pieces (such as Czerniewicz et al., 2020 ; Jandrić et al., 2020 ; Joseph & Trinick, 2021 ) and reports not published in peer-reviewed journals (such as Ferdig et al., 2020 ); these exclusions are not a judgment on either the quality or the level of insight of such pieces, nor on the modes of research and scholarship that they embody. This decision, as well as the focus on studies published in English and Chinese, limit the extent to which this review covers the experiences of ERT technology use by teacher populations across the world.

Due to our international remit, another limitation is the integration of findings grounded in different local contexts and HE environments. We overcome this partially by extracting from each paper the context in which teachers’ technology use is situated and taking such information into account when narratively integrating data across studies and presenting our review findings (see Appendix 5 ). However, the inconsistent terminology used to allude to the notions of ‘technology’ and ‘emergency remote teaching’ in the reviewed articles poses a major challenge to our cross-context comparison [see discussion on the jingle-jangle fallacy in Sum and Oancea ( 2021 )]. Another review conducted by Bond et al. ( 2021 ) also found at least ten different terms used for ‘online teaching’ (including ‘emergency remote teaching’) in their selected papers.

Although uniformly agreed-upon definitions of these terms are absent (Singh & Thurman, 2019 ), the nuances of concepts underlying them have not been given due consideration in the majority of the studies reviewed (see “ Description of included articles ” section). Further terminological complexity arises from the imperfect overlap between Chinese and English vocabularies. Whilst we tried to overcome this by extracting information on each study’s conceptualisation of ‘technology’ and ‘ERT’ (see Appendix 5 ) and accompanying translations with original Chinese terms (for example, the phrase ‘线上教学’ in Chinese can be sometimes translated into ‘online teaching and learning’), we acknowledge that terminological and translation gaps remain in our cross-context synthesis of the selected literature.

Description of included articles

Included in our final synthesis are 32 empirical research studies covering 71 countries and reporting perspectives from 4725 HE teachers altogether. Of these, the largest proportion focuses on the HE context in Asia (n = 15), followed by Europe (n = 7) and Africa (n = 6) (see Table ​ Table2). 2 ). Given our inclusion of articles indexed in Chinese databases, Mainland China alone is the focal context of n = 5 studies. A wide range of subject areas in HASS disciplines are covered (see Table ​ Table3). 3 ). Studies using qualitative data are most common (n = 14) (see Table ​ Table4), 4 ), and a sample size of fewer than 50 teachers is often reported (n = 21) (see Table ​ Table5). 5 ). Appendix 6 presents a summary of the characteristics of included studies.

Geographical distribution of the included studies (N = 32)

a To avoid double counting, this paper has only been counted once, under ‘Americas’

Disciplinary areas in the included articles

Research approach of included articles

Higher education teacher sample size of the included articles

Exactly half of the studies (n = 16) have a local remit (see Table ​ Table6), 6 ), amongst which many recruited fellow academics from the authors’ institutions (n = 14). As noted by several researchers in their papers, the public health emergency and its concomitant restrictions had in various ways altered the methods for research and data collection, including shifting to a local focus whilst access to other settings was limited.

Remit of included articles

Authors of three quarters of the reviewed studies (n = 24) obtained data from participants remotely, either by phone or online. Much empirical data were collected in a space that was relatively new and unfamiliar to the researcher and the researched during a time when both individuals were coping with not only the expected expeditious embrace of various technologies for ERT but also, amongst other things, the physical and psychological burden posed by the coronavirus pandemic. Hence, this review integrates, in a systematic and holistic fashion, data from the discrete, often inevitably limited, yet valiant research initiatives undertaken in different countries during the periods of drastic increases in infections and deaths at the incipient phase of the COVID-19 outbreak.

In terms of substantive focus, whilst most of the included studies describe ‘what’ and/or ‘how’ technologies were being used by teachers during ERT (n = 14) and offer a dichotomous pros-or-cons narrative of technology use for ERT (n = 21), often vis-à-vis in-person teaching prior to COVID-19, some (n = 7) also examine the wider implications for teachers and HE at large.

Due partly to the novelty of COVID-19 and the haste with which research was conducted, the conceptualisation of technology and its relation with remote teaching in times of COVID-19 is either weak or largely absent in the majority of the reviewed studies. Technologically deterministic views seem prevalent in the literature reviewed. Many studies place ‘technology’ as the centre of inquiry and underscore the palpable ‘impact’ that various technical objects impose on teaching. For example, the attribution of recent pedagogical innovations and educational developments to technological advancements features prominently in the introductory paragraphs of numerous papers. Some assert that the emergence of social networking sites has begun to direct all walks of life including the ways in which teaching has been carried out since before the pandemic. Additionally, the discussion of ‘technology-enabled’ and ‘technology-enhanced’ teaching used in some articles implies that ‘technology’ plays an almost indispensable role in teaching and that teaching would be seriously disrupted without it. In contrast, there was little awareness in many of these papers of the extent to which technologies may carry political or commercial agendas or may be underpinned by complex ideologies and social structures (Selwyn et al., 2020 ). This echoes the conclusions of pre-COVID research by An and Oliver ( 2021 ) and Costa et al. ( 2019 ) that theoretical understanding of ‘technology’ in educational research is under-developed.

A brief narrative of ERT experiences from teachers’ perspectives

An eclectic range of technological artefacts and their uses during ERT across HE settings is reported in the studies. Cases of initial technology use range widely from straightforward approaches such as uploading teaching materials online to (mis)uses such as creating excessive recorded lectures and assignments. What is common, however, across reports in most studies is the acutely negative sentiments of intimidation, angst, confusion, and even despair of ERT amongst teachers at the outset of the transitioning period. It gave teachers great shock and pain to make a forced, often slapdash migration to ERT—a terrain that many of them were unfamiliar with and uncertain of—whilst juggling with their home and other work responsibilities during the distressing period. In addition to the psychological burden, teachers were worried about the well-being of their students, particularly those from underprivileged backgrounds and in vulnerable environments. Across HE settings worldwide, teachers had on average less than a week’s preparation time, leaving them feeling woefully unprepared. Hence, it is unsurprising that the majority of teachers in the studies reviewed found the immediate phase of migration to ERT burdensome and emotionally exhausting. Yet, some sought a silver lining and considered ERT as a creative challenge and an opportunity for a long-needed meaningful reflection and overhaul of HE teaching practices.

We mapped each included article’s findings about teachers’ overall attitudes towards ERT using the World Bank’s classification of country development (2020) (see Table ​ Table7). 7 ). For studies not examining teachers’ attitudes directly, we inferred negative attitudes from teachers’ reports of dissatisfaction and frustrations over the challenges in ERT, and any indication of concern and anxiety; positive attitudes were inferred from teachers’ expressions of satisfaction and awareness of benefits brought by ERT, and any indication of optimism and hope.

Teachers’ overall attitudes towards emergency remote teaching (ERT) and its concomitant technology use as implied in the articles reviewed (categorised based on World Bank ( 2020 )’s country classification by income)

a We categorise teachers’ attitudes as reported by each paper into five categories (namely ‘mostly negative’, ‘more negative’, ‘mixed response’, ‘more positive’, and ‘mostly positive’) by weighing the strength of evidence for both positive and negative attitudes of teachers reported in and/or inferred from each included study. For example, for teachers’ attitudes to be categorised as ‘mostly negative’, the paper has to (1) present strong evidence for negative attitudes from teachers’ reports of dissatisfaction and frustrations over the challenges in ERT, and any indication of concern and anxiety, and (2) present little or no evidence for positive attitudes which can be inferred from teachers’ expressions of satisfaction and awareness of benefits brought by ERT, and any indication of optimism and hope

Reports by teachers from higher-income countries/regions were more positive whilst those from lower-and middle-income countries/regions tended to be more negative, though with a few exceptions (for example, teachers in mainland China had relatively positive emotional responses and teachers of hearing-impaired students in high-income Saudi Arabia reported overwhelmingly negative emotional responses during the ERT period). In propitious circumstances, teachers’ emotional responses could change substantially over time from apprehension, frustration, and pessimism to relief, affirmation, and an eventual sense of achievement. Sometimes, as teachers gradually became conversant with various technological artefacts and encountered a suitable way of teaching, either serendipitously or after multiple experimentation, they eventually saw ERT as a humbling and rewarding experience. Some teachers evaluated the pedagogical revisions they made during ERT positively and even expressed the intention to keep part of their teaching online or expected to continue to use the technologies employed for ERT in the future.

Factors shaping technology use by teachers in ERT across HE contexts

The 32 papers reviewed include results on qualitative and quantitative factors identified by teacher participants that potentially shape teachers’ technology use in ERT. Note that these are not always empirically validated, nor explicitly identified as ‘factors’ in the included articles (particularly in qualitative accounts they may be described as reasons, drivers, challenges, barriers, and conditions). Thus, we adopted an open and inclusive definition of factors based on the implied or explicit direction of influence on ERT, and we grouped them thematically. Summary accounts of these thematic groupings based on the data presented in the review corpus are discussed below in descending order of the respective strength of evidence in the reviewed studies (see full references in Table ​ Table8 8 ).

Thematic groupings of identified potential factors shaping higher education teachers’ technology use in COVID-19 emergency remote teaching implied in the reviewed studies

Social-technological factors

Whilst Tartavulea et al. ( 2020 ) note that the transition to ERT can be facilitated by having online platforms and facilities, they also found that access to electronic devices and internet connection can be a luxury. Frequently reported technical concerns by teachers include the unreliability of network conditions, lack of devices and equipment, and limitations of digital infrastructure. These issues are not only powerful barriers to technology use in emergency teaching but they also disproportionately affect teachers and students in lower-income countries/regions. Note, however, that even in the context of an affluent country like the United States, teachers and students may report inequitable access to the necessities of ERT from home (Cutri et al., 2020 ; Sales et al., 2020 ).

Beneath the surface of these technical difficulties are the imbalanced allocation of resources and entrenched socio-economic problems which most commonly beset lower-and middle-income countries and regions (Tanga et al., 2020 ). Whilst the issues teachers face are highly contextualised, a considerable number of students come from underprivileged backgrounds. Even before the pandemic hit, these students had been confronting different challenges such as, particularly in lower-income countries, frequent commute of several miles from rural areas to the city for internet connection. Even if internet access were provided at home, these students would still need to overcome problems of intermittent or no power supply in their localities. In addition, during lockdowns they may shoulder more home-care responsibilities, sometimes in overcrowded or even abusive home environments.

Some teachers were also amongst vulnerable groups and had limited access to the internet at home, for example due to the sharing of cellular data with household members, and therefore exposed themselves to greater health risks by visiting commercial establishments such as cafés with free internet provision in order to teach remotely. Compounding this predicament is that HE teachers reported that they often had little information about students’ backgrounds, which hindered their efforts to address students’ educational and psychological needs and any equity issues pertinent to their studies (Cutri et al., 2020 ). These technical complications are situated in specific social contexts and have been a major hindrance to technology use in ERT.

Institutional factors

In most of the studies reviewed, the migration to ERT was described as mandatory, and teachers’ use of certain applications was often resultant from policies imposed by their institutions—whose regulations on teaching could be heavily influenced by government decisions, for example in universities in Mainland China (Tang et al., 2020 ). To ensure continuity and safety of teaching and learning in times of upheaval and uncertainty, some HEIs exercised greater control over the ways in which technologies were used in teaching, such as mandating the use of certain Learning Management Systems (LMS) in teaching (Khoza & Mpungose, 2020 ) or prohibiting asynchronous methods of teaching (Cutri et al., 2020 ). Whilst some teachers felt that their creative freedoms to use different technologies in their teaching were constrained by institutional policies , others sought detailed guidance and perceived the lack of clear institutional protocols as a significant barrier to technology use in this emergency (Sobaih et al., 2020 ).

Aside from policy, different forms of institutional support (such as the provision of digital infrastructure and training for both teachers and students) could also be of value to teachers in ERT, although the level of support felt by teachers could vary by discipline (Watermeyer et al., 2021 ). However, the value of technical assistance might be undermined when technology specialists were just as confused as teachers about teaching remotely in emergency times (Gyampoh et al., 2020 ; Tanga et al., 2020 ). Another gap in institutional support pointed out by some studies is the lack of recognising teachers’ hardship and efforts in teaching in the form of pecuniary (such as support for procurement of equipment) and non-pecuniary rewards (such as teaching awards) (Joshi et al., 2020 ).

Individual factors

Sometimes teachers resisted institutional policies and employed instead other technologies of their own preference. Individual factors therefore play an important role in shaping teachers’ technology use. Despite the challenges posed by the pandemic, some teachers were tolerant of uncertainties, valiantly departing from their previous pedagogical praxis and forging ahead with ‘pedagogical agility’ (Kidd & Murray, 2020 )—the flexibility of adapting to the new teaching conditions in rapid yet meaningful ways. Resilient and adaptive, these teachers ‘rolled up their sleeves’ and worked around the clock to seek teaching solutions and countermeasures through constant, active self-exploration (Sales et al., 2020 ). Some music teachers, for instance, would make immediate remedies for the connection disruptions to synchronous lessons by providing students with recordings of their playing as examples (Akyürek, 2020 ). In an Israeli college, teacher educators incorporated topics like ‘distance learning’ into the teacher training curriculum to reflect the new circumstances of teaching (Hadar et al., 2021 ). One teacher educator even painted a wall at home with special paint to make it into a ‘blackboard’ where his writings were presented and screened to students (Hadar et al., 2021 ). These are just a few of the many manifestations of teachers’ agentic creativity and ongoing inventiveness in innovating their own use of technologies and resources despite the presence of severe constraints in ERT times.

In terms of readiness, despite receiving considerable institutional support in some cases, teachers often felt ill-prepared for ERT and doubtful of their abilities in using various technologies to teach (Scherer et al., 2021 ), and only a minority felt rather ready for ERT (Alqabbani et al., 2020 ). The studies reviewed discussed the variation in teachers’ readiness for ERT in relation to gender, academic discipline, and country context (Scherer et al., 2021 ). For example, in predominantly high-income economies teachers moved from a customary integration of technologies in pre-COVID teaching to fully-online ERT (Mideros, 2020 ; Sales et al., 2020 ). But not all teachers and students had had the opportunities to familiarise themselves with various technologies (including otherwise widely used applications like Word processing) prior to COVID-19 (Gyampoh et al., 2020 ). Whilst experienced online teachers felt more prepared and expected themselves to employ more frequently a wide array of technologies in teaching, across HE contexts many teachers had seriously limited prior experience in ‘online teaching’ and were apprehensive about using technologies for teaching purposes (Bailey & Lee, 2020 ). Besides, being experienced in ‘online teaching’ does not necessarily translate to successful handling of ERT, given the limited time frame and the stressful and even traumatising circumstances at the outset of the crisis.

Pedagogical factors

Across HE settings, teachers considered how to connect and engage dislocated groups of students through technologies, how to empower students to explore beyond the curriculum as students gained more control over what and how they study in the shifting context of teaching and learning (Mideros, 2020 ), and how to reconfigure spaces in ways that provide students with a nourishing, inter-connected intellectual environment despite being physically apart during the ERT period (Kidd & Murray, 2020 ). In Australia, teachers were especially concerned about first-year students, as the southern hemisphere’s Autumn 2020 was their very first term at the university. In addition to providing students with considered feedback, these teachers employed strategies such as the online polls and hand-raising functions on various EdTech platforms (Zeng, 2020 ), or made students the host of Blackboard Collaborate in order for teaching to be more engaging (Marshalsey & Sclater, 2020 ).

As coronavirus infections spread, teachers also attended to students’ emotional and educational well-being. Some teacher educators in the United Kingdom offered one-on-one tutorials online to establish personal connections with student teachers and monitor their progress (Kidd & Murray, 2020 ). A teacher in Pakistan went the extra mile to care for the students living in far-flung areas without internet access by sending them CD recordings of their lectures (Said et al., 2021 ). In Saudi Arabia, teachers of hard-of-hearing students used a special configuration of multiple spaces to enable the inclusion of synchronous sign-language translation in their online lectures (Alsadoon & Turkestani, 2020 ). In cases where the discrepancy between technology use by teachers and students was significant, teachers would often bridge the gap by adapting and adopting technologies (such as social media) that they were not always conversant with, but which were most used and preferred by students. As a teacher participant put it, teachers have ‘to go where [students] are, and not wait for [students] to come to where [they] are’ (Sales et al., 2020 , p. 13).

Often teachers would consider the compatibility of certain technologies with their teaching philosophies and practices within their disciplines. Teacher educators in Israel, for example, might feel additional pressure from the expectation that their pedagogical use of technologies has to set examples for their student teachers (Hadar et al., 2021 ). As another example, teaching translation/interpretation in Mainland China was especially challenging during the ERT period since teachers have to demonstrate to students the operation of simultaneous interpretation equipment and the use of dual-track recording function—which is not commonly found in existing online applications (Ren, 2020 ).

Peer factors

Teachers reported that they saw their colleagues as not only sources of inspiration for technology use, but also remedies for stress and uncertainty during the ERT period (Ren, 2020 ). Unlike in prior ‘online teaching’ where they could still meet in person to discuss technology use, many teachers struggled with technological learning-by-doing in relative isolation during the COVID-19 lockdown period (Cutri et al., 2020 ). In view of the absence of physical spaces for colleagues to informally exchange professional practices and channel their emotionality and empathy for one another (Cutri et al., 2020 ; Scherer et al., 2021 ), some teachers put in deliberate effort into organising new networking spaces to bring the academic community together online. In an attempt to alleviate the uncertainties brought by ERT and their adverse impact on psychological well-being, teachers worked together remotely as a team to explore solutions and share useful insights about technology use in teaching. They felt empowered by the constant encouragement and motivational texts from their peers (Ren, 2020 ). Teachers thrived on establishing connections with technology-proficient colleagues whose technical expertise and guidance were relied upon (Bailey & Lee, 2020 ; Mouchantaf, 2020 ) and whose ingenious engagement with technologies inspired and were even assimilated into their own teaching practices. As a mitigation strategy to ease teachers’ hasty migration into ERT, mutual empowerment through facilitated discussions amongst colleagues meaningfully shaped the ways technologies were used by teachers in ERT.

Interplay of factors

Whilst we have delineated potential factors shaping technology use in ERT in a linear, point-by-point fashion, this list of non-exhaustive items should not be conceived as separate, stand-alone factors since they interact in a complex and nuanced way across various contexts. For instance, having little institutional support and no access to LMS or students’ information, some teachers in public HEIs in Egypt resorted to reaching students through popular social media. Teachers then explored on their own the ways in which they could continue teaching activities via these platforms which were new to them (Sobaih et al., 2020 ). As for teachers in an Israeli college, upon realising some Arabic female students refused to appear online due to their cultural values, they made allowance for students’ decisions to keep their cameras off (Hadar et al., 2021 ). But the inability to read students’ expressions during class added to the teaching challenges during ERT and demanded additional flexibility and pedagogical adjustments from teachers. Therefore, technology use is influenced by the combined factors of students’ socio-cultural backgrounds and teachers’ resources and adaptability to changes. In addition to the complex interplay of these factors, these examples demonstrate that teachers’ technology use in ERT is heavily contextualised across HE settings and should therefore be understood in its wider cultural embedding and socio-economic contexts.

Implications of technology use in ERT for teachers

As for our second research question, the studies reviewed indicate that the implications of technology use in ERT for teachers are manifold. These findings are categorised into pedagogical, work-related, and cross-cutting implications, discussed below (see Table ​ Table9 9 for a summary table).

Implications of technology use in COVID-19 emergency remote teaching for teachers as implicated in the reviewed studies

Pedagogical implications

With the paradoxical amalgam of being ‘together but (physically) apart’ (Marshalsey & Sclater, 2020 ) in the new COVID-19 context of teaching, the notions of space and time, as well as the dynamics of the classroom and teacher-student relationship, have undergone less palpable yet important changes.

Spatiality-wise, teachers realised the loss of important physical spaces and the erosion of values traditionally attached to these spaces during the transition to ERT. Marshalsey and Sclater ( 2020 ), for example, reason how a physical art and design studio embodies a distinctive set of values, resources, and the signature experiential hands-on pedagogical practice of their discipline. But when artworks are presented online, their materiality, colours, and texture may be diminished.

Temporality-wise, some teachers felt a strongly contorted notion of time which rendered futile any discussion on the ordinary longitudinal perception of ‘being ready for teaching’ (Cutri et al., 2020 ). Not only was the migration to ERT perceived as rushed and disorganised but teachers also felt time as short, discrete intervals when many changes could occur. Some even found it difficult to find ‘a point of reference for their sense of self as experienced professionals’ (Cutri et al., 2020 , p. 533). This new sense of temporality is perhaps most concisely summarised by a comment made by a teacher during ERT: ‘I always plan a month ahead. Now I live from one day to the next’ (Hadar et al., 2021 , p. 454).

Within this new spatial–temporal context, teachers often felt that student engagement in remote teaching and learning activities was superficial and unequally distributed (Joshi et al., 2020 ; Kidd & Murray, 2020 ). Deprived of in-person interaction, teachers can neither hear the voices nor see the expressions of all students, and find the classroom discourse to be dominated by students who are generally more confident in sharing their ideas in front of the whole class (Hadar et al., 2021 ; Marshalsey & Sclater, 2020 ). With the loss of informal physical spaces where students used to ask questions and interact further with teachers before and after class (Cutri et al., 2020 ), some teachers commented that both teachers and students were more likely to stay in their ‘echo chambers’ during the pandemic (Eringfeld, 2021 ).

Teachers adopted different strategies to navigate being outside the comfort zone of the physical classroom. Some attempted to retain or increase control over interactions in the remote ‘classroom’ (Mideros, 2020 ) such as by only letting students speak when allowed (Gyampoh et al., 2020 ) and shifting to a predominantly teacher-centric, didactic approach of lecturing because of the perceived difficulty of implementing hands-on training in an exclusively remote teaching environment (Cutri et al., 2020 ). The students, too, adopted their own strategies, often distinct from their teachers’ (Callo & Yazon, 2020 ; Sobaih et al., 2020 ). As some students generally adapted to ERT with relative ease (Mideros, 2020 ; Ren, 2020 ), sometimes they even used technology as a defensive wall to exclude teachers (who were in some cases less tech-savvy than their students) from being involved in their studies during the pandemic (Sales et al., 2020 ). Many teachers in the studies reviewed reported that the mandated use of various technologies in ERT puts a strain on pedagogy, the major implications of which may include an elevated feeling of detachment from the class, a heightened distance from students (Kidd & Murray, 2020 ), and a more pronounced gap in teacher-student interactions (Callo & Yazon, 2020 ; Sales et al., 2020 ).

Moreover, ERT is thought to have precipitated the collapse of ‘yishigan’ (仪式感)—a Chinese expression which, when applied to this context, refers to the sense that teaching is a special, ritualised occasion (Lu, 2020 ; Ren, 2020 ). As ‘yishigan’ abates in the context of ERT, so does the sense of formality and immediacy felt by teachers and students, both of whom may no longer view teaching and learning as a serious, formalised routine of life in the same way as before; some of the studies reviewed note that motivation and classroom engagement are lowered as a result of this change in perception (see examples in Joshi et al., 2020 ; Lu, 2020 ; Marshalsey & Sclater, 2020 ).

In contrast with the sense of limitation, hierarchy, and loss illustrated by the accounts summarised above, other teachers reported a sense of the ‘intimacy of distance’ and a less visible teacher-student hierarchy as a combined result of emergency technology use during the pandemic. Such teachers valued the creation of spaces for more student-oriented and student-empowering pedagogy. In Mainland China, for example, the classroom atmosphere was livened up as students were encouraged by teachers to engage in class via alternative forms of interaction online such as sending emojis, raising ‘hands’, and taking polls (Gao & Zhang, 2020 ; Zeng, 2020 ). In other contexts, teachers felt an idiosyncratic sense of closeness as they shared a screen and read the same text with students on their devices (Eringfeld, 2021 ). They also reported a better understanding of students’ personal circumstances, home environment, and even household responsibilities as students turned on their cameras in class (Hadar et al., 2021 ; Kidd & Murray, 2020 ). In many ways, teachers observed their students being more relaxed in class, which enabled teachers to build personal relationships with their students in ways that they had never envisioned before (Marshalsey & Sclater, 2020 ).

Because of the collapse of ‘yishigan’ and the resultant casual and more relaxed classroom dynamics in the new spatiality, some teachers adapt to the ‘online etiquette’ by using emojis and GIFs when communicating with students (Marshalsey & Sclater, 2020 ). Also, the fact that students may be more technology-competent than teachers meaningfully shifts the dynamic of the teacher-student relationship in the ERT classroom (Kidd & Murray, 2020 ), for teachers often solicited help from students on questions regarding technology use, and during this process teachers increasingly saw students as their partners in teaching rather than subordinates to themselves (Cutri et al., 2020 ). As Cutri et al. ( 2020 ) remark, ‘the negative connotations of risk-taking and making mistakes while learning to teach online seem to have been mitigated by a combination of affective factors such as humility, empathy, and even optimism’ (p. 523). As an experience of vulnerability, ERT has grounded and humbled teachers, allowing them to develop both more appreciation for self-care (Eringfeld, 2021 ), and more empathy for students (Khoza & Mpungose, 2020 ; Kidd & Murray, 2020 ).

Teachers realised the salience of exercising care for students and themselves and considering the emotionality of students, especially those in vulnerable states (Alqabbani et al., 2020 ; Sales et al., 2020 ). Pastoral care took priority during particularly distressing periods when students were most in need of emotional support (Sobaih et al., 2020 ; Tejedor et al., 2020 ). All these examples suggest that under the new spatial–temporal reorientation an intricate web of human relations has evolved and, to varying degrees, been revitalised.

Work-related implications

The task of transitioning teaching to an alternative mode is only one of the many challenges teachers face in the larger contexts of academia during the pandemic period (Cutri et al., 2020 ). Although the extra time seemingly freed up by, say, the lack of commutes is highly valued for student support, self-care or family care (Eringfeld, 2021 ; Kidd & Murray, 2020 ; Tejedor et al., 2020 ), there has also been an excessive intensification of workload in preparation for ERT (Khan et al., 2020 ; Lu, 2020 ; Mouchantaf, 2020 ; Said et al., 2021 ), and this is expected to last for a few years into the post-ERT era (Watermeyer et al., 2021 ). When working from home, teachers received as many as hundreds of students’ inquiries throughout the day via various applications (Alsadoon & Turkestani, 2020 ; Sobaih et al., 2020 ). Coupled with the pressure to prove that work has been conducted remotely (Kidd & Murray, 2020 ; Marshalsey & Sclater, 2020 ), some teachers report feeling compelled to be present online around the clock. The ‘timelessness’ of working remotely in a home setting has been succinctly summarised by a teacher: ‘it is too easy to “just send one more email”’ (Watermeyer et al., 2021 ). The praxis and boundaries of academic work were shifted and reconstructed in ways many perceived as intrusive into the personal life sphere and deteriorative to work-life balance and also teachers’ well-being and occupational welfare (Watermeyer et al., 2021 ).

In addition, with looming financial challenges to the HE sector, casualised and untenured staff reported an elevated feeling of job precarity because their extra commitment to teaching cuts into time for other academic work, such as publishing research—which they perceived as often prioritised over teaching efforts in HE career progression (Cutri et al., 2020 ). Some reported that these teachers’ vulnerability was compounded by the management’s misperception that teaching remotely during emergency lightens teachers’ workload, and by their misinterpretation that low scores given by students on evaluations of ERT are a marker of ‘teacher quality’ rather than a way for students to express disinclination towards ERT in general (Watermeyer et al., 2021 ).

Technology use in ERT was further complicated by the need for swift re-coordination of private routines and domestic spaces to make room for professional work. A teacher, for example, asked all household members to disconnect from the Wi-Fi when teaching (Kidd & Murray, 2020 ). Having a separate, free-of-disturbance workspace at home is a luxury that not many teachers could afford (Gyampoh et al., 2020 ; Joshi et al., 2020 ) especially in contexts like Pakistan where joint families may live together in a crowded household (Said et al., 2021 ). Due to the non-separation of home/workspaces, customary parameters between the private and public domains were being reconstituted, and the boundaries between teachers’ personal and professional identities became blurry (Khoza & Mpungose, 2020 ). Consequently, female academics with caring responsibilities were disproportionately affected, and increasingly teachers found themselves struggling to perform either role well (Watermeyer et al., 2021 ).

In the larger context of HE, teachers were also worried about the ‘placelessness’ of HE during lockdowns and that the role of HE as an embodied, communal space for teaching and learning, self-formation, and socialisation was being undermined (Eringfeld, 2021 ). In two studies based in the UK (Eringfeld, 2021 ; Watermeyer et al., 2021 ), the accounts of their teacher participants add up to a strong ‘dystopian’ rhetoric, reflecting their fears that the ERT migration epitomises the beginning of a prolonged contraction of HE as an on-campus experience and monetisation of part of the HE experience driven largely by massification but not quality, thereby undermining the core academic values and humanising aims of HE.

Not all studies reviewed painted a consistently gloomy picture of the work-related implications of ERT and technology use. Some studies note that the compulsory, emergency move to remote teaching may have offered multiple opportunities. For example, in some propitious circumstances, teachers were able to constitute their networking spaces online to channel mutual support and facilitate exchanges on technology use. There are also reports that more trust was placed on technology specialists, technicians, and younger faculty who were often seen as more technologically adept and relied upon during ERT (Watermeyer et al., 2021 ). Moreover, the infrastructural divisions that used to separate departments on a physical campus are largely dismantled with the migration to ERT, enabling possibilities of various forms of inter-departmental communication and cross-disciplinary collaboration (Tejedor et al., 2020 ) and thereby making HE a flatter-structured and less hierarchically-organised workplace for teachers (Eringfeld, 2021 ).

Cross-cutting implications

Some of the teachers in the studies reviewed commented on the potential of ERT to undermine the ethos of the academic profession and imperil the work of academics. They noted that ERT could be pedagogically regressive, as teachers’ role may be reduced to merely technical functions, such as uploading materials online. This challenged their beliefs about what good teaching entails and compromised their often long-established pedagogical practices (Watermeyer et al., 2021 ). Other teachers struggled with balancing depth in their teaching with what they saw as their students’ preference for over-simplified yet visually appealing inputs such as bite-sized explanations shared on TikTok and other social media (Sales et al., 2020 ). Some anticipate worrying trends of ‘dumbing down’ of HE if teaching continues to be impersonal, disembodied and mediated predominantly by digital technologies in the post-ERT era (Watermeyer et al., 2021 ).

We have discussed so far the changes to HE teaching due to the relocation to newly formed spaces, as reported in the studies reviewed. Yet, some principles and values that teachers apply to guide their teaching practices remained unchanged amidst the ongoing crisis. These include the upholding of integrity, academic transparency, privacy, and other ethical principles in teaching (Mouchantaf, 2020 ). For example, teachers were concerned about the potential collection of students’ data for third-party use without prior informed consent (Diningrat et al., 2020 ; Joshi et al., 2020 ). Others also recognise the importance for students of using technology responsibly (Gyampoh et al., 2020 ) and being equipped with critical and reflective thinking capacity to evaluate the accuracy and relevance of information online (Sales et al., 2020 ; Tejedor et al., 2020 ), including resisting the temptation to reuse others’ ideas as their own work (Dampson et al., 2020 ) and refraining from using improper language on social media (Ghounane, 2020 ; Sobaih et al., 2020 ). This was especially relevant during the absence of teacher’s in-person monitoring, when the responsibility to access and study educational materials was partially shifted to students (Gyampoh et al., 2020 ), many of whom were inclined to explore topics of interest on their own (Marshalsey & Sclater, 2020 ; Mideros, 2020 ; Sales et al., 2020 ).

For teachers themselves, their practical wisdom and professional deliberation to ‘consider when, why, and how to use technology properly’ (Diningrat et al., 2020 , p. 706) were put to the test during the emergency contexts of teaching. A teacher participant in the study by Cutri et al. ( 2020 ) shared his belated reflection on an inadvertent, frivolous ridicule he had made about a student’s slow internet speed in front of the entire class online. This anecdote alludes to two problems looming in the wider context of HE teaching: (1) the largely absent code of conduct that delineates appropriate practices and roles of teachers and students in the new spatiality (and this can be due partly to the short time horizon in ERT); and (2) the difficulty for teachers to create supportive yet private spaces to address equity issues and attend to students’ emotionality in strict confidence when being online (Cutri et al., 2020 ).

Teachers participating in the studies reviewed in this paper indicated a multiplicity of factors that interacted to shape their technology use during the ERT period. In line with Liu et al. ( 2020 )’s pre-pandemic work, we find strong evidence that technology use in teaching is a context-sensitive, socially-embedded topic of study and hence should be understood in the socio-political, cultural and material context in which academics and students are situated (Selwyn et al., 2020 ). For example, the label ‘technical issues’ could encompass a wide range of contextualised problems, from power outages to long commutes for Internet access, from material shortages to widespread hunger, from trenchant poverty to deep-seated structured inequalities, which afflict disproportionately relatively poor, underserved communities and the most disadvantaged segments of populations (Chan et al., 2022 ) but are also palpable within higher-income countries/regions [see, for example, Cullinan et al. ( 2021 ) for a study on broadband access disparities in Ireland].

The narrative account we constructed is indicative of the resourcefulness and resilience of teachers to continue teaching during the crisis, even those in marginalised communities where resources are limited. This view is also shared by Padilla Rodríguez et al. ( 2021 ) who study the changes teachers in rural Mexico have made to their teaching practice in response to the suspension of in-person classes without receiving much external support during the pandemic. Around the world, teachers forayed into ERT during times of uncertainty by seeking to empower themselves and exploring various technological artefacts in teaching on their own, on the one hand; and by endorsing mutual empowerment and drawing inspiration from amongst their peers, on the other. Their collective efforts in supporting one another in the wake of crisis created what Matthewman and Uekusa ( 2021 ) call ‘disaster communitas’, which temporarily served to support teachers when adapting to the hasty conversion to ERT. We concur with Hickling et al. ( 2021 ) that the creation of a supportive space and environment for HE teachers to commiserate, discuss experiences, and share insights and resources with colleagues helps advance teaching practices with technology.

In answering the second research question, we have discussed at length the implications of a more encompassing use of technology in ERT and how evolving notions of space and time combined to reconstitute teacher-student relationships and the nature of academics’ work (Williamson et al., 2020 ). The studies reviewed indicate that the rushed transition to ERT has affected the sense of professional identity of academics as HE teachers (Littlejohn et al., 2021 ) in ways that are as yet only partly explored. Echoing the findings of Ramlo ( 2021 ), we believe that teachers’ negotiation of the blurring home-workspace boundaries (Blumsztajn et al., 2022 ; Littlejohn et al., 2021 ) and attempts to rebalance their professional work and personal life have important implications for future HE teaching and merit further investigation (Gourlay et al., 2021 ).

As COVID-19 continues to take a toll on people’s lives, we draw on the studies reviewed to emphasise the importance of re-prioritising the value of social and emotional connections in HE teaching, as well as the overall well-being of both teachers and students (Baker et al., 2022 ; Yeung & Yau, 2021 ). ‘Networks of care’ between teachers and students as well as amongst teachers themselves may be constructed to ameliorate uncertainties brought by the pandemic (Czerniewicz et al., 2020 ; Joseph & Trinick, 2021 ). Elements of care can be developed by simple acts of kindness (Murray et al., 2020 ) and gestures to communicate approachability (Glantz et al., 2021 ), all of which contribute to constructing more supportive and less hierarchical teacher-student relationships in the digital context. We note, however, that evidence scattered across the studies reviewed indicates that academic recognition and reward systems have not accounted well for the creative efforts that academics (including casualised and untenured staff) have put into teaching and maintaining relationships with their colleagues and students in response to the ongoing challenges ensuing from the coronavirus crisis. This is another priority for HEIs and leadership teams. On a final note, future research may explore further, innovative ways in which HE teaching can be reconstituted in the presence and context of technology without undermining teachers’ professional identity or compromising the revitalisation of teaching as an embodied, communal, and humanising experience as campuses around the world re-open, in full or in part, for in-person activities in post-pandemic times.

Appendix 1. A detailed version of inclusion/exclusion criteria

Appendix 2. Search terms in English and Chinese (note that the search strategy varied slightly across databases due to the different limits they set on the length of search input)

Appendix 3. PRISMA 2020 flow diagram for systematic review (Page et al., 2021 )

Appendix 4. Quality and relevance assessment rubric and the average scores of the 32 included studies (adapted from Oancea et al., 2021 )

a Score description: 4—criterion fully met; 3—criterion mostly met, though with some weaknesses; 2—criterion only partly met, with several or serious weaknesses; 1—criterion largely not met

Appendix 5. Data extraction grid

Appendix 6. Summary of characteristics of 32 reviewed studies

a The references of four articles show the publication year of 2021. These four articles were published online ahead of print in 2020 and hence are included in this study

Acknowledgements

The corresponding author gave a presentation on the preliminary findings of this systematic review at the 1st International Yidan Prize Doctoral Conference (online) organized by the University of Oxford on 27 May 2021. The insightful questions raised by the audience are gratefully acknowledged. We would like to thank Dr. Victoria Elliott, Ms. Renyu Jiang, Ms. Abbey Palmer, and Ms. Catherine Scutt who have directly and indirectly provided their support for this research project.

Additional information

The corresponding author is a doctoral candidate reading Education. This paper is an original work, conducted by the corresponding author in parallel to the preparation for submission of a thesis for a Doctor of Philosophy (DPhil) degree under the supervision of the second author. Preliminary findings of this systematic review have been published in the Proceedings of the Yidan Prize Doctoral Conference under the terms of a Creative Commons Attribution License (CC-BY) (see Sum & Oancea, 2021 ).

Author contributions

Under the guidance and supervision of AO, MS performed all stages of the systematic review, from conceptualising the review project to writing the manuscript. Both authors worked collaboratively from late 2020 to mid 2022 on this project. MS and AO independently coded and analysed a selection of data excerpts at various stages to check for inter-rater reliability as mentioned in ‘ Methodology ’ section. The rubric for quality assessment was based on past work by AO. Communications between the authors were maintained throughout the research process. MS worked on drafting this paper, which was subsequently revised by the AO. Both authors read and approved the final manuscript.

This work was generously supported by a scholarship jointly awarded by the Clarendon Fund and New College of the University of Oxford (2020–2023).

Availability of data and materials

Declarations.

The authors declare that they have no competing interests.

Publisher's Note

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Contributor Information

McQueen Sum, Email: [email protected] .

Alis Oancea, Email: [email protected] .

• Adedoyin OB, Soykan E. Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments. 2020 doi: 10.1080/10494820.2020.1813180. [ CrossRef ] [ Google Scholar ]
• Akyürek R. The views of lecturers about distance music education process in the pandemic period. International Journal of Education Technology and Scientific Researches. 2020; 5 (13):1790–1833. doi: 10.35826/ijetsar.262. [ CrossRef ] [ Google Scholar ]
• Alqabbani S, Almuwais A, Benajiba N, Almoayad F. Readiness towards emergency shifting to remote learning during COVID-19 pandemic among university instructors. E-Learning and Digital Media. 2020; 18 (5):460–479. doi: 10.1177/2042753020981651. [ CrossRef ] [ Google Scholar ]
• Alsadoon E, Turkestani M. Virtual classrooms for hearing-impaired students during the COVID-19 pandemic. Romanian Journal for Multidimensional Education. 2020; 12 (1, Sup. 2):1–8. doi: 10.18662/rrem/12.1sup2/240. [ CrossRef ] [ Google Scholar ]
• Amunga J. Leveraging technology to enhance STEM education amidst the Covid-19 pandemic: An overview of pertinent concerns. Technium Social Sciences Journal. 2021; 18 (1):40–55. doi: 10.47577/tssj.v18i1.3044. [ CrossRef ] [ Google Scholar ]
• An T, Oliver M. What in the world is educational technology? Rethinking the field from the perspective of the philosophy of technology. Learning, Media and Technology. 2021; 46 (1):6–19. doi: 10.1080/17439884.2020.1810066. [ CrossRef ] [ Google Scholar ]
• Bailey DR, Lee AR. Learning from experience in the midst of covid-19: Benefits, challenges, and strategies in online teaching. Computer-Assisted Language Learning Electronic Journal. 2020; 21 (2):176–196. [ Google Scholar ]
• Baker S, Anderson J, Burke R, De Fazio T, Due C, Hartley L, Molla T, Morison C, Mude W, Naidoo L, Sidhu R. Equitable teaching for cultural and linguistic diversity: Exploring the possibilities for engaged pedagogy in post-COVID-19 higher education. Educational Review. 2022 doi: 10.1080/00131911.2021.2015293. [ CrossRef ] [ Google Scholar ]
• Blumsztajn A, Koopal W, Rojahn P, Schildermans H, Thoilliez B, Vlieghe J, Wortmann K. Offline memos for online teaching: A collective response to The manifesto for teaching online (Bayne et al. 2020) Postdigital Science and Education. 2022 doi: 10.1007/s42438-022-00286-4. [ CrossRef ] [ Google Scholar ]
• Bond M, Bedenlier S, Marín VI, Händel M. Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education. 2021; 18 :50. doi: 10.1186/s41239-021-00282-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Broadbent J, Poon WL. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education. 2015; 27 :1–13. doi: 10.1016/j.iheduc.2015.04.007. [ CrossRef ] [ Google Scholar ]
• Callo EC, Yazon AD. Exploring the factors influencing the readiness of faculty and students on online teaching and learning as an alternative delivery mode for the new normal. Universal Journal of Educational Research. 2020; 8 (8):3509–3518. doi: 10.13189/ujer.2020.080826. [ CrossRef ] [ Google Scholar ]
• Chakraborty P, Mittal P, Gupta MS, Yadav S, Arora A. Opinion of students on online education during the COVID-19 pandemic. Human Behavior and Emerging Technologies. 2021; 3 (3):357–365. doi: 10.1002/hbe2.240. [ CrossRef ] [ Google Scholar ]
• Chan RY, Bista K, Allen RM, editors. Online teaching and learning in higher education during COVID-19: International perspectives and experiences. Routledge; 2022. [ Google Scholar ]
• Costa C, Hammond M, Younie S. Theorising technology in education: An introduction. Technology, Pedagogy and Education. 2019; 28 (4):395–399. doi: 10.1080/1475939X.2019.1660089. [ CrossRef ] [ Google Scholar ]
• Crawford J, Butler-Henderson K, Rudolph J, Malkawi B, Glowatz M, Burton R, Magni PA, Lam S. COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching. 2020; 3 (1):9–28. doi: 10.37074/jalt.2020.3.1.7. [ CrossRef ] [ Google Scholar ]
• Cullinan J, Flannery D, Harold J, Lyons S, Palcic D. The disconnected: COVID-19 and disparities in access to quality broadband for higher education students. International Journal of Educational Technology in Higher Education. 2021; 18 :26. doi: 10.1186/s41239-021-00262-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cutri RM, Mena J, Whiting EF. Faculty readiness for online crisis teaching: Transitioning to online teaching during the COVID-19 pandemic. European Journal of Teacher Education. 2020; 43 (4):523–541. doi: 10.1080/02619768.2020.1815702. [ CrossRef ] [ Google Scholar ]
• Czerniewicz L, Agherdien N, Badenhorst J, Belluigi D, Chambers T, Chili M, de Villiers M, Felix A, Gachago D, Gokhale C, Ivala E, Kramm N, Madiba M, Mistri G, Mgqwashu E, Pallitt N, Prinsloo P, Solomon K, Strydom S, Swanepoel M, Waghid F, Wissing G. A wake-up call: Equity, inequality and Covid-19 emergency remote teaching and learning. Postdigital Science and Education. 2020; 2 (3):946–967. doi: 10.1007/s42438-020-00187-4. [ CrossRef ] [ Google Scholar ]
• Dampson DG, Addai-Mununkum R, Apau SK, Bentil J. COVID-19 and online learning: A SWOT analysis of users’ perspectives on learning management system of University of Education, Winneba, Ghana. International Journal of Learning, Teaching and Educational Research. 2020; 19 (9):382–401. doi: 10.26803/ijlter.19.9.20. [ CrossRef ] [ Google Scholar ]
• Dedeilia A, Sotiropoulos MG, Hanrahan JG, Janga D, Dedeilias P, Sideris M. Medical and surgical education challenges and innovations in the COVID-19 era: A systematic review. In Vivo. 2020; 34 (3 suppl):1603–1611. doi: 10.21873/invivo.11950. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dhawan S. Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems. 2020; 49 (1):5–22. doi: 10.1177/0047239520934018. [ CrossRef ] [ Google Scholar ]
• Diningrat S, Nindya M, Salwa S. Emergency online teaching: Early childhood education lecturers’ perception of barrier and pedagogical competency. Jurnal Cakrawala Pendidikan. 2020; 39 (3):705–719. doi: 10.21831/cp.v39i3.32304. [ CrossRef ] [ Google Scholar ]
• Eringfeld S. Higher education and its post-coronial future: Utopian hopes and dystopian fears at Cambridge University during Covid-19. Studies in Higher Education. 2021; 46 (1):146–157. doi: 10.1080/03075079.2020.1859681. [ CrossRef ] [ Google Scholar ]
• Ferdig RE, Baumgartner E, Hartshorne R, Kaplan-Rakowski R, Mouza C, editors. Teaching, technology, and teacher education during the COVID-19 pandemic: Stories from the field. Association for the Advancement of Computing in Education; 2020. [ Google Scholar ]
• Gao LX, Zhang LJ. Teacher learning in difficult times: Examining foreign language teachers’ cognitions about online teaching to tide over COVID-19. Frontiers in Psychology. 2020; 11 :549653. doi: 10.3389/fpsyg.2020.549653. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gaur U, Majumder MAA, Sa B, Sarkar S, Williams A, Singh K. Challenges and opportunities of preclinical medical education: COVID-19 crisis and beyond. SN Comprehensive Clinical Medicine. 2020; 2 (11):1992–1997. doi: 10.1007/s42399-020-00528-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ghounane N. Moodle or social networks: What alternative refuge is appropriate to Algerian EFL students to learn during COVID-19 pandemic. Arab World English Journal. 2020; 11 (3):21–41. doi: 10.24093/awej/vol11no3.2. [ CrossRef ] [ Google Scholar ]
• Glantz, E., Gamrat, C., Lenze, L., & Bardzell, J. (2021). Improved student engagement in higher education’s next normal. EDUCAUSE Review . https://er.educause.edu/articles/2021/3/improved-student-engagement-in-higher-educations-next-normal
• Gordon M, Patricio M, Horne L, Muston A, Alston SR, Pammi M, Thammasitboon S, Park S, Pawlikowska T, Rees EL, Doyle AJ, Daniel M. Developments in medical education in response to the COVID-19 pandemic: A rapid BEME systematic review: BEME Guide No. 63. Medical Teacher. 2020; 42 (11):1202–1215. doi: 10.1080/0142159X.2020.1807484. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gough D, Oliver S, Thomas J. Introducing systematic reviews. In: Gough D, Oliver S, Thomas J, editors. An introduction to systematic reviews. 2. Sage; 2017. pp. 1–18. [ Google Scholar ]
• Gourlay L, Littlejohn A, Oliver M, Potter J. Lockdown literacies and semiotic assemblages: Academic boundary work in the Covid-19 crisis. Learning, Media and Technology. 2021; 46 (4):377–389. doi: 10.1080/17439884.2021.1900242. [ CrossRef ] [ Google Scholar ]
• Granić A, Marangunić N. Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology. 2019; 50 (5):2572–2593. doi: 10.1111/bjet.12864. [ CrossRef ] [ Google Scholar ]
• Gyampoh AO, Ayitey HK, Fosu-Ayarkwah C, Ntow SA, Akossah J, Gavor M, Vlachopoulos D. Tutor perception on personal and institutional preparedness for online teaching-learning during the COVID-19 crisis: The case of Ghanaian Colleges of Education. African Educational Research Journal. 2020; 8 (3):511–518. doi: 10.30918/AERJ.83.20.088. [ CrossRef ] [ Google Scholar ]
• Hadar LL, Alpert B, Ariav T. The response of clinical practice curriculum in teacher education to the Covid-19 breakout: A case study from Israel. Prospects. 2021; 51 :449–462. doi: 10.1007/s11125-020-09516-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hickling S, Bhatti A, Arena G, Kite J, Denny J, Spencer NL, Bowles DC. Adapting to teaching during a pandemic: Pedagogical adjustments for the next semester of teaching during COVID-19 and future online learning. Pedagogy in Health Promotion. 2021; 7 (2):95–102. doi: 10.1177/2373379920987264. [ CrossRef ] [ Google Scholar ]
• Higher Education Statistical Agency. (n.d.). The higher education classification of subjects (HECoS) . Retrieved from https://www.hesa.ac.uk/support/documentation/hecos
• Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The difference between emergency remote teaching and online learning. EDUCAUSE Review . https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
• Jandrić P, Hayes D, Truelove I, Levinson P, Mayo P, Ryberg T, Monzó LD, Allen Q, Stewart PA, Carr PR, Jackson L, Bridges B, Escaño C, Grauslund D, Mañero J, Lukoko HO, Bryant P, Fuentes-Martinez A, Gibbons A, Hayes S. Teaching in the age of Covid-19—1 year later. Postdigital Science and Education. 2020; 3 (3):1073–1223. doi: 10.1007/s42438-021-00243-7. [ CrossRef ] [ Google Scholar ]
• Joseph D, Trinick R. ‘Staying apart yet keeping together’: Challenges and opportunities of teaching during COVID-19 across the Tasman. New Zealand Journal of Educational Studies. 2021; 56 (2):209–226. doi: 10.1007/s40841-021-00211-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Joshi A, Vinay M, Bhaskar P. Impact of coronavirus pandemic on the Indian education sector: Perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education. 2020; 18 (2):205–226. doi: 10.1108/ITSE-06-2020-0087. [ CrossRef ] [ Google Scholar ]
• Khan R, Bashir A, Basu BL, Uddin ME. Emergency online instruction at higher education in Bangladesh during COVID-19: Challenges and suggestions. The Journal of Asia TEFL. 2020; 17 (4):1497–1506. doi: 10.18823/asiatefl.2020.17.4.26.1497. [ CrossRef ] [ Google Scholar ]
• Khoza SB, Mpungose CB. Digitalised curriculum to the rescue of a higher education institution. African Identities. 2020 doi: 10.1080/14725843.2020.1815517. [ CrossRef ] [ Google Scholar ]
• Kidd W, Murray J. The Covid-19 pandemic and its effects on teacher education in England: How teacher educators moved practicum learning online. European Journal of Teacher Education. 2020; 43 (4):542–558. doi: 10.1080/02619768.2020.1820480. [ CrossRef ] [ Google Scholar ]
• Lee J, Jung I. Instructional changes instigated by university faculty during the COVID-19 pandemic: The effect of individual, course and institutional factors. International Journal of Educational Technology in Higher Education. 2021; 18 :52. doi: 10.1186/s41239-021-00286-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Littlejohn A, Gourlay L, Kennedy E, Logan K, Neumann T, Oliver M, Potter J, Rode JA. Moving teaching online: Cultural barriers experienced by university teachers during Covid-19. Journal of Interactive Media in Education. 2021; 1 (7):1–15. doi: 10.5334/jime.631. [ CrossRef ] [ Google Scholar ]
• Liu Q, Geertshuis S, Grainger R. Understanding academics’ adoption of learning technologies: A systematic review. Computers & Education. 2020; 151 :103857. doi: 10.1016/j.compedu.2020.103857. [ CrossRef ] [ Google Scholar ]
• Lu XC. Jiyu shiping huiyi pingtai de yuancheng tongbu kouyi jiaoxue: Yi beiwai gaofan tongsheng chuanyi kecheng wei li [Distance teaching of interpreting: Delivering simultaneous interpreting courses via video conferencing at Graduate School of Translation and Interpretation, Beijing Foreign Studies University] Zhongguo Fanyi. 2020; 42 (4):76–84. [ Google Scholar ]
• Marshalsey L, Sclater M. Together but apart: Creating and supporting online learning communities in an era of distributed studio education. International Journal of Art & Design. 2020; 39 (4):826–840. doi: 10.1111/jade.12331. [ CrossRef ] [ Google Scholar ]
• Matthewman S, Uekusa S. Theorizing disaster communitas. Theory and Society. 2021; 50 (6):965–984. doi: 10.1007/s11186-021-09442-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mercader C, Gairín J. University teachers' perception of barriers to the use of digital technologies: The importance of the academic discipline. International Journal of Educational Technology in Higher Education. 2020; 17 :4. doi: 10.1186/s41239-020-0182-x. [ CrossRef ] [ Google Scholar ]
• Mideros D. Out-of-class learning of Spanish during COVID-19: A case study in Trinidad and Tobago. Studies in Self-Access Learning Journal. 2020; 11 (3):119–219. doi: 10.37237/110308. [ CrossRef ] [ Google Scholar ]
• Mittal A, Mantri A, Tandon U, Dwivedi YK. A unified perspective on the adoption of online teaching in higher education during the COVID-19 pandemic. Information Discovery and Delivery. 2021 doi: 10.1108/IDD-09-2020-0114. [ CrossRef ] [ Google Scholar ]
• Mok KH, Xiong W, Bin Aedy Rahman HN, H. N. COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong. International Journal of Chinese Education. 2021 doi: 10.1177/22125868211007011. [ CrossRef ] [ Google Scholar ]
• Mouchantaf M. The COVID-19 pandemic: Challenges faced and lessons learned regarding distance learning in Lebanese higher education institutions. Theory and Practice in Language Studies. 2020; 10 (10):1259–1266. doi: 10.17507/tpls.1010.11. [ CrossRef ] [ Google Scholar ]
• Murray C, Heinz M, Munday I, Keane E, Flynn N, Connolly C, Hall T, MacRuairc G. Reconceptualising relatedness in education in ‘distanced’ times. European Journal of Teacher Education. 2020; 43 (4):488–502. doi: 10.1080/02619768.2020.1806820. [ CrossRef ] [ Google Scholar ]
• Na S, Jung H. Exploring university instructors’ challenges in online teaching and design opportunities during the COVID-19 pandemic: A systematic review. International Journal of Learning, Teaching and Educational Research. 2021; 20 (9):308–327. doi: 10.26803/ijlter.20.9.18. [ CrossRef ] [ Google Scholar ]
• Oancea, A., McDermott, T., Robson, J., Scutt, C., Xu, X., Mun, O., Nuseibeh, N. & Voss, M. (2021). The landscape of educational research in the UK. Report to the Royal Society and British Academy joint enquiry on educational research. London: Royal Society and British Academy.
• Padilla Rodríguez BC, Armellini A, Traxler J. The forgotten ones: How rural teachers in Mexico are facing the COVID-19 pandemic. Online Learning. 2021; 25 (1):253–268. doi: 10.24059/olj.v25i1.2453. [ CrossRef ] [ Google Scholar ]
• Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021; 372 (71):n71. doi: 10.1136/bmj.n71. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pokhrel S, Chhetri R. A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future. 2021; 8 (1):133–141. doi: 10.1177/2347631120983481. [ CrossRef ] [ Google Scholar ]
• Rajab MH, Gazal AM, Alkattan K. Challenges to online medical education during the COVID-19 pandemic. Cureus. 2020; 12 (7):e8966. doi: 10.7759/cureus.8966. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ramlo S. The coronavirus and higher education: Faculty viewpoints about universities moving online during a worldwide pandemic. Innovative Higher Education. 2021; 46 (3):241–259. doi: 10.1007/s10755-020-09532-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ren W. Yiqing he hou yiqing shidai de kouyi jiaoxue: Jiyu jiaoshi shijiao de anli fenxi yu fansi [Interpretation studies in the pandemic and post-pandemic times: A case analysis and reflections based on teachers’ perspectives] Zhongguo Fanyi. 2020; 42 (6):69–74. [ Google Scholar ]
• Resch K, Alnahdi G, Schwab S. Exploring the effects of the COVID-19 emergency remote education on students’ social and academic integration in higher education in Austria. Higher Education Research & Development. 2022 doi: 10.1080/07294360.2022.2040446. [ CrossRef ] [ Google Scholar ]
• Said F, Ali I, Javed T. An interpretative phenomenological analysis of challenges faced by the university teachers in Pakistan amid Covid-19. International Journal of Educational Research and Innovation. 2021; 15 :260–272. doi: 10.46661/ijeri.5256. [ CrossRef ] [ Google Scholar ]
• Salas‐Pilco, S. Z., Yang, Y., Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID‐19 pandemic: A systematic review. British Journal of Educational Technology, 53 (3), 593–619. [ PMC free article ] [ PubMed ]
• Sales D, Cuevas-Cerveró A, Gómez-Hernández JA. Perspectives on the information and digital competence of social sciences students and faculty before and during lockdown due to Covid-19. Profesional De La Información. 2020; 29 (4):e290423. doi: 10.3145/epi.2020.jul.23. [ CrossRef ] [ Google Scholar ]
• Scherer R, Howard SK, Tondeur J, Siddiq F. Profiling teachers’ readiness for online teaching and learning in higher education: Who's ready? Computers in Human Behavior. 2021; 118 :106675. doi: 10.1016/j.chb.2020.106675. [ CrossRef ] [ Google Scholar ]
• Selwyn N. Education and technology: Key issues and debates. Bloomsbury Publishing; 2022. [ Google Scholar ]
• Selwyn N, Hillman T, Eynon R, Ferreira G, Knox J, Macgilchrist F, Sancho-Gil JM. What’s next for Ed-Tech? Critical hopes and concerns for the 2020s. Learning, Media and Technology. 2020; 45 (1):1–6. doi: 10.1080/17439884.2020.1694945. [ CrossRef ] [ Google Scholar ]
• Singh V, Thurman A. How many ways can we define online learning? A systematic literature review of definitions of online learning (1988–2018) American Journal of Distance Education. 2019; 33 (4):289–306. doi: 10.1080/08923647.2019.1663082. [ CrossRef ] [ Google Scholar ]
• Singh-Pillay A, Naidoo J. Context matters: Science, technology and mathematics education lecturers’ reflections on online teaching and learning during the COVID-19 pandemic. Journal of Baltic Science Education. 2020; 19 (6A):1125–1136. doi: 10.33225/jbse/20.19.1125. [ CrossRef ] [ Google Scholar ]
• Sobaih AEE, Hasanein AM, Abu Elnasr AE. Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability. 2020; 12 (16):6520. doi: 10.3390/su12166520. [ CrossRef ] [ Google Scholar ]
• Stewart WH. A global crash-course in teaching and learning online: A thematic review of empirical emergency remote teaching (ERT) studies in higher education during year 1 of COVID-19. Open Praxis. 2021; 13 (1):89–102. doi: 10.5944/openpraxis.13.1.1177. [ CrossRef ] [ Google Scholar ]
• Sum, M., & Oancea, A. (2021). Higher education teachers’ perspectives on technology use in emergency remote teaching during the global pandemic: A systematic literature review. In Proceedings of the Yidan Prize Doctoral Conference (pp. 103-124). Department of Education, University of Oxford. https://yidanprize.org/files/Proceedings-of-the-2021-Yidan-Prize-Doctoral-Conference.pdf .
• Talib MA, Bettayeb AM, Omer RI. Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review. Education and Information Technologies. 2021; 26 :6719–6746. doi: 10.1007/s10639-021-10507-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tang C, Zhou XY, Qian XH. Yiqing fangkong qijian difang gaoxiao zaixian jiaoxue de shijian yu sikao – yi Chengdu shifan xueyuan ‘1-3-5-4’ fang’an weili [The path and practice of promoting online teaching in local undergraduate universities during the epidemic prevention and control period—Taking Chengdu Normal University ‘1-3-5-4’ scheme as an example] Xiandai Jiaoyu Jishu. 2020; 27 (8):120–126. [ Google Scholar ]
• Tanga P, Ndhlovu GN, Tanga M. Emergency remote teaching and learning during Covid-19: A recipe for disaster for social work education in the Eastern Cape of South Africa? African Journal of Social Work. 2020; 10 (3):17–24. [ Google Scholar ]
• Tartavulea CV, Albu CN, Albu N, Dieaconescu RI, Petre S. Online teaching practices and the effectiveness of the educational process in the wake of the COVID-19 pandemic. Amfiteatru Economic. 2020; 22 (55):920–936. doi: 10.24818/EA/2020/55/920. [ CrossRef ] [ Google Scholar ]
• Tejedor S, Cervi L, Tusa F, Parola A. Education in times of pandemic: Reflections of students and teachers on virtual university education in Spain, Italy, and Ecuador. Latin Journal of Social Communication. 2020; 78 :19–40. doi: 10.4185/RLCS-2020-1466. [ CrossRef ] [ Google Scholar ]
• UNESCO Institute for Statistics. (2012). International standard classification of education: ISCE, 2011. UNESCO Institute for Statistics. Retrieved from http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf
• Watermeyer R, Crick T, Knight C, Goodall J. COVID-19 and digital disruption in UK universities: Afflictions and affordances of emergency online migration. Higher Education. 2021; 81 (3):623–641. doi: 10.1007/s10734-020-00561-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williamson B, Eynon R, Potter J. Pandemic politics, pedagogies and practices: Digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology. 2020; 45 (2):107–114. doi: 10.1080/17439884.2020.1761641. [ CrossRef ] [ Google Scholar ]
• World Bank. (June 2020). List of economies (with classification of country development) [Database]. https://databank.worldbank.org/data/download/site-content/CLASS.xls
• Wu W, Yao R, Xie Z. Gaoxiao jiaoshi zaixian jiaoxue jingli dui ziwo jiaoxue pingjia de yingxiang - jiyu quanguo 334 suo gaoxiao zaixian jiaoxue de diaocha fenxi [The influence of university teachers’ online teaching experience on their self-evaluation of teaching: A survey in 334 universities] Gaodeng Jiaoyu Yanjiu. 2020; 41 (8):63–72. [ Google Scholar ]
• Yeung MW, Yau AH. A thematic analysis of higher education students’ perceptions of online learning in Hong Kong under COVID-19: Challenges, strategies and support. Education and Information Technologies. 2021 doi: 10.1007/s10639-021-10656-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zeng LW. Yiqing fangkong beijing xia xianshang jiaoxue shishi xiaoguo ji yingxiang yinsu fenxi – jiyu Guangdong sheng gaoxiao de diaocha [Analysis of the effectiveness and influencing factors of online teaching in the context of pandemic prevention and control—A survey based on universities in Guangdong province] Gaoxiao Tansuo. 2020; 27 (7):85–91. [ Google Scholar ]

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• Funding Programs
• Public Wireless Supply Chain Innovation Fund

Innovation Fund Round 2 (2024) Open RU

Program overview.

The Innovation Fund aims to foster competition and innovation across the global telecommunications ecosystem, lower costs for consumers and network operators, and strengthen the 5G and successor wireless technology supply chains. The program’s objectives include unlocking opportunities for innovative companies, particularly small and medium enterprises, to compete in a market historically dominated by a few suppliers, some of which present a high security risk to the U.S. and its partners.

The Innovation Fund facilitates these goals by advancing the adoption of open and interoperable wireless networks. Open and interoperable wireless networks offer numerous benefits over traditional, closed networks that will lower barriers of entry for new and emerging companies. Using open and interoperable wireless networks allows operators to procure the best solutions for their specific needs by mixing and matching network components, increasing competition, and driving innovation.

Key Focus Areas

This Notice of Funding Opportunity (NOFO) is the second in a series that the National Telecommunications and Information Administration (NTIA) will issue and administer under the Innovation Fund. This NOFO is divided into two (2) specific research focus areas (SRFAs):  

• SRFA 1: – Open Radio Unit (RU) Commercialization: Projects funded under SRFA 1 will focus on accelerating the development of open RU products to the point where they meet carrier needs and are ready for commercial trials; and 
• SRFA 2: – Open RU Innovation: Projects funded under SRFA will focus on improving the overall performance and capabilities of open RUs through targeted research and development.  

Innovation Fund NOFO 2 Program Snapshot

You can visit the Innovation Fund Program NOFO 2 Snapshot page or download a one-page summary of the Public Wireless Supply Chain Innovation Program NOFO 2 Snapshot by clicking on the button below

Funding Availability  

NTIA will award up to $420,000,000 under this NOFO. The amount of funding NTIA expects to award per project differs by SRFA as follows:

• $25,000,000 – $45,000,000 per project.
• $5,000,000 – $10,000,000 per project.

Application Period

Applications were due no later than 11:59 p.m. (EDT) on July 17, 2024. The due date and time reflected a one-week application submission extension from the original deadline.

Eligible Use(s) of Funds

Grant recipients may only use Federal award funds and any non-federal cost share committed by the recipient to pay for allowable costs under the Innovation Fund program. Allowable costs are determined by the provisions outlined in the NOFO and in accordance with the Federal cost principles identified in 2 C.F.R. § 200.400, Subpart E . In addition, costs must be reasonable, necessary, allocable, and allowable for the proposed project, and must conform to generally accepted accounting principles as defined in 2 C.F.R. § 200.400, Subpart E .

Who Can Apply

Eligibility varies for each SRFA as follows:

SRFA 1: Eligible entities include U.S. and international for-profit companies, non-profit companies, institutions of higher education, industry groups, and partnerships consisting of two or more such entities. In addition, applicants must meet the following requirements:

• They must be capable of production and commercial sale of Open RUs. Capability must be demonstrated in application materials.
• They must partner with at least one (1) Mobile Network Operator (MNO).
• They must demonstrate their intent and ability to deploy Open RU(s) at scale after the period of performance.
• The applicant or MNO partner or both must have Ultimate Beneficial Ownership (UBO) in the United States, its Territories, or its Possessions.
• Open RU development work must occur in the United States, its Territories, or its Possessions. Laboratory or field testing work, conducted with the MNO, may occur overseas.

The period of performance (POP) for SRFA 1 awards is expected to be 18-24 months. The POP may be less than 18 months, but not more than 24 months from the date of award.

SRFA 2: Eligible Entities include U.S. and international for-profit companies, non-profit companies, institutions of higher education, industry groups, and partnerships consisting of two or more such entities. All work under SRFA 2 must occur in the U.S. The POP for SRFA 2 awards is expected to be three to five years. The POP may be less than three years, but not more than five years from the date of award.

How to Apply  

Applicants must submit their materials via Grants.gov. NTIA encourages applicants to initiate the following two steps early, so they are set up for success during the NOFO application window.

• You must register with SAM.gov  to obtain a Unique Entity Identifier (UEI). An active registration is required at time of award. This process may take weeks so please ensure registration is done in sufficient time so that it does not impact your ability to meet submission deadlines.
• Register an account with Grants.gov . You must have obtained your UEI before registering on Grants.gov. Please fill in all organizational applicant information for the profile of the account. Communication regarding the grant will only be sent to the point of contact (POC) listed on the organizational profile.  

You can contact the Innovation Fund via Innovation Fund's email address .

Related content

More than 225 applications totaling nearly $3 billion submitted for the wireless innovation fund.

WASHINGTON – The Department of Commerce’s National Telecommunications and Information Administration (NTIA) announced today that it received 227 applications requesting more than $2.94 billion in funding to support wireless equipment innovation.

Frequently Asked Questions #2

Transforming spectrum sharing: ntia seeks to fund innovation in software defined radio technology.

By: Charles Cooper, Associate Administrator, Office of Spectrum Management

NTIA’s new round of funding from the $1.5 billion Public Wireless Supply Chain Innovation Fund presents a unique opportunity to advance spectrum-sensing technology, in turn potentially driving more efficient use of airwaves for the public and private sectors.

Spectrum sharing is an effective way to increase commercial access to spectrum resources while protecting and enhancing government operations. Spectrum-sensing technology incorporated into a sharing system or framework could protect incumbent operators while making more spectrum available for other beneficial uses. These sensor technologies would be able to detect and identify government radio signals among all of the various radio signals and avoid interfering with them.  

One objective of this funding opportunity ’s second focus area is to propel the development of advanced Software Defined Radio (SDR) technology that meets the needs of modern mobile networks. Projects funded under this initiative will target several critical advancements:

• Read more about Transforming Spectrum Sharing: NTIA Seeks to Fund Innovation in Software Defined Radio Technology