smart university literature review and creative analysis

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2018 | Buch

Smart Universities

Concepts, systems, and technologies.

herausgegeben von: Vladimir L. Uskov, Jeffrey P. Bakken, Robert J. Howlett, Lakhmi C. Jain

Verlag: Springer International Publishing

Buchreihe : Smart Innovation, Systems and Technologies

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Über dieses Buch

This book presents peer-reviewed contributions on smart universities by various international research, design and development teams. Smart university is an emerging and rapidly evolving area that creatively integrates innovative concepts; smart software and hardware systems; smart classrooms with state-of-the-art technologies and technical platforms; smart pedagogy based on modern teaching and learning strategies; smart learning and academic analytics; as well as various branches of computer science and computer engineering.

The contributions are grouped into several parts: Part 1—Smart Universities: Literature Review and Creative Analysis, Part 2—Smart Universities: Concepts, Systems and Technologies, Part 3—Smart Education: Approaches and Best Practices, and Part 4—Smart Universities: Smart Long Life Learning. The book is a valuable source of research data and findings, design and development outcomes, and best practices for faculty, scholars, Ph.D students, administrators, practitioners and anyone interested in the rapidly growing areas of smart university and smart education.

Inhaltsverzeichnis

Frontmatter, chapter 1. innovations in smart universities, smart universities: literature review and creative analysis, chapter 2. smart university: literature review and creative analysis, smart universities: concepts, systems and technologies, chapter 3. smart university: conceptual modeling and systems’ design, chapter 4. smart university: software systems for students with disabilities, chapter 5. building a smarter college: best educational practices and faculty development, chapter 6. building smart university using innovative technology and architecture, smart education: approaches and best practices, chapter 7. practicing interprofessional team communication and collaboration in a smart virtual university hospital, chapter 8. edlets: towards smartness in math education, chapter 9. a framework for designing smarter serious games, chapter 10. using a programming exercise support system as a smart educational technology, smart universities: smart long life learning, chapter 11. the role of e-portfolio for smart life long learning, chapter 12. towards smart education and lifelong learning in russia, chapter 13. knowledge building conceptualisation within smart constructivist learning systems, premium partner.

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This book presents peer-reviewed contributions on smart universities by various international research, design and development teams. Smart university is an emerging and rapidly evolving area that creatively integrates innovative concepts; smart software and hardware systems; smart classrooms with state-of-the-art technologies and technical platforms; smart pedagogy based on modern teaching and learning strategies; smart learning and academic analytics; as well as various branches of computer science and computer engineering. The contributions are grouped into several parts: Part 1Smart Universities: Literature Review and Creative Analysis, Part 2Smart Universities: Concepts, Systems and Technologies, Part 3Smart Education: Approaches and Best Practices, and Part 4Smart Universities: Smart Long Life Learning. The book is a valuable source of research data and findings, design and development outcomes, and best practices for faculty, scholars, Ph.D students, administrators, practitioners and anyone interested in the rapidly growing areas of smart university and smart education.

• Publication Years 2000 - 2018
• Publication counts 27
• Citation count 5
• Available for Download 0
• Downloads (cumulative) 36
• Downloads (12 months) 0
• Downloads (6 weeks) 0
• Average Downloads per Article 0
• Average Citation per Article 0
• Publication Years 2017 - 2018
• Publication counts 2
• Citation count 0
• Downloads (cumulative) 0
• Publication Years 2010 - 2024
• Publication counts 70
• Citation count 6
• Publication Years 2004 - 2024
• Publication counts 134
• Citation count 52

Index Terms

Applied computing

Enterprise computing

Human-centered computing

Human computer interaction (HCI)

Interaction paradigms

Web-based interaction

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Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis

Friday joseph agbo.

1 School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101 Joensuu, Finland

Solomon Sunday Oyelere

2 Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden

Jarkko Suhonen

Markku tukiainen, associated data.

The datasets generated during this study are available from the corresponding author.

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

The evolution of learning and teaching methods from the traditional classroom learning environment to a technology-enhanced learning environment positively impacts education (Cárdenas-Robledo & Peña-Ayal, 2018 ; McIntosh, Herman, Sanford, McGraw, & Florence, 2004 ). This transition is even more relevant nowadays due to unforeseen circumstances that create an emergency on the world’s education, for example, where formal learning is not possible due to closure of schools as experienced in the recent COVID-19 pandemic (Atchison et al., 2020 ). As a result of this pandemic and to prevent the spread of the disease, many countries adopted online distance learning as an alternative teaching model (Reimers & Schleicher, 2020 ). This situation underscores the importance of developing a flexible, personalized, and adaptive learning environment to facilitate learning and teaching anytime, anywhere without physical contact and limited human interventions.

Research has shown that smart learning environments (SLE) can provide a twenty-first-century learning environment powered by advanced technology (Kim, Cho, & Lee, 2012 ; Laine & Joy, 2009 ), pedagogy (Tomczyk et al., 2019 ), and creative strategies (Harris, Dousay, Hall, Srinivasan, & Srinivasan, 2020 ). Thus, SLE promises to provide the future learning ecosystem by leveraging advanced learner models and evolving new technology. Smart learning environments refers to ubiquitous, context-aware, personalized, and intelligent system capable of providing a high level of motivation, engagement, and intelligent feedback for a better learning experience (Agbo et al., 2019 ; Hwang, 2014 ). The emerging field of smart learning environments began to gain scholars’ attention in recent times. The increasing growth of the field creates the opportunity to investigate the smart learning environments trends in the literature and how its discussion among scholars has progressed. A comprehensive review of literature in smart learning environments is very important. First, it will provide overview of the progress made by scholars and their status. Second, it will reveal critical information that can guide researchers in making decision regarding areas to focus their future research (field hotspots); and which publication outlet is suitable for publication. To this end, this study examines the research landscape of the smart learning environment to gain a comprehensive understanding of the research activities from a multidisciplinary perspective, trends, and possible future direction of the field.

Wang et al. ( 2020 ) recently conducted a related study that examined the research trend, status in the field of smart learning within China from 2012 to 2019. These authors, (Wang et al., 2020 ) were interested in knowing when research in smart learning began in China, its trend, and scholars’ publication contributions. The problem with this paper is that it is limited to a country and date bound. While our study derives motivation from Wang et al. ( 2020 ), it takes a different approach by conducting a comprehensive and all-encompassing study that is not limited to specific date ranges, regions, or countries. Besides, this study is focused on the science mapping of literature from the Scopus database by using the Bibliometric approach (Esfahani, Tavasoli, & Jabbarzadeh, 2019 ; Gilani, Salimi, Jouyandeh, Tavasoli, & Wong, 2019 ). Science mapping technic with Bibliometrix R-package is a useful approach to performing the Bibliometric analysis of scientific publications (Aria & Cuccurullo, 2017 ). A bibliometric study has been acclaimed to provide a useful tool for analysing the evolution of discipline based on its intellectual contributions, social, and conceptual structure (Zupic & Čater, 2015 ). Besides, many similar studies have applied bibliometric analysis to present an overview of specific field research. Among these studies, it is worthy of mentioning some recent and related areas such as research landscape of learning analytics (Waheed, Hassan, Aljohani, & Wasif, 2018 ), augmented reality research (Arici, Yildirim, Caliklar, & Yilmaz, 2019 ), multimedia learning research (Li, Antonenko, & Wang, 2019 ), and research on classroom dialogue (Song et al., 2019 ). These studies identified most outstanding publications, publication outlets, prolific scholars, research topics, and trends in the respective fields.

Research objectives

This study aims to present a comprehensive review of the smart learning environment; hence, a bibliometric analysis is appropriate. To the best of our knowledge, no extensive bibliometric study of literature on smart learning environments has been conducted. This study is the first to conduct a bibliometric analysis of the field with a specific objective to examine the trend of smart learning environments over time; investigate the themes of smart learning in the publications; recognize prolific scholars and their contribution in the field of the smart learning environment; explore publication networks and collaborations across institutions, countries, and regions over time. Additionally, the study intends to identify any shift in the smart learning environment field’s boundaries from a large body of information in extant research.

The outcome of this study will provide useful knowledge for young scholars, mostly the young ones who are just starting to research in the field of smart learning environments. For example, young researchers can quickly identify top articles in terms of the number of citations, prolific authors, and research hotspots. Besides information such as trending topics and thematic future direction of smart learning environments can stimulate young researchers’ decision in terms of research interest. The main research question that this study seeks to answer is: how research in the field of smart learning environments has progressed over the years in terms of scientific productions, thematic breakthroughs, scholars’ contributions, and future thematic direction?

In this study, a bibliometric mapping analysis was conducted. Bibliometric mapping is recently gaining more grounds in different disciplines (Aria & Cuccurullo, 2017 ; Arici et al., 2019 ; Song et al., 2019 ). Perhaps, the suitability of bibliometric for science mapping may have caused this extending acceptance among scholars (Aria & Cuccurullo, 2017 ). The entire procedure for conducting bibliometric mapping analysis in this study including data collection, screening, extraction, and synthesis are presented in this section.

Literature search and data collection

First, we commenced by conducting a document search on the Scopus database. The search string consists of a combination of compound keywords concatenated with the OR operator. The first search field contained the keywords “smart learning environment” to search “All fields,” while the second search field contained keywords such as adaptive, context*, personalized, and intelligent. These additional keywords in the second line of the search field were selected because they are mostly used to define the characteristic features of smart learning environments (Hwang, 2014 ). Besides, these keywords have been associated with smart learning. For instance, Molina-Carmona and Villagr-Arnedo ( 2018 ) in their study entitled “smart learning”, emphasized keywords such as “personalized learning”, “adaptive learning, situation or context-aware learning as key orchestrates smart learning environments. The initial query without any filtering returned 1212 document results. The search and retrieval of the data were conducted on June 19, 2020. These results were later filtered to exclude some irrelevant items based on our inclusion and exclusion criteria. The inclusion and exclusion criteria are presented in Table  1 . The search string combinations, operators, and filtering using the criteria explained in Table ​ Table1 1 is shown below.

Inclusion and exclusion criteria for retrieving the dataset

(ALL(“smart learning environment”) OR TITLE-ABS-KEY(“adaptive context* personalized intelligent”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO(DOCTYPE, “ar”) OR LIMIT-TO(DOCTYPE, “cp”) OR LIMIT-TO(DOCTYPE,"ch”)) AND (EXCLUDE(PUBSTAGE, “aip”)) AND (EXCLUDE(SRCTYPE, “d”)) .

As the database was limited to Scopus, authors do not claim that an exhaustive list of data was acquired. The possibility of missing out on data from other databases such as Web of Science, PubMed, ERIC, etcetera could be minimal if a compatible formatting standard that allows for merging data generated from independent databases exists. Unfortunately, the bibliometrix R-package software 1 used in this study does not currently support this ambition. However, Scopus covers a large number of articles and provides higher records in terms of citations (Heradio et al., 2016 ; Shen & Ho, 2020 ). Consequently, we claim that sufficient data to outline the scientific landscape, research hotspots, and other analysis conducted in this study was retrieved.

Data extraction, loading, and conversion

In total, 1081 data were collected after refining based on the inclusion and exclusion criteria shown in Table ​ Table1. 1 . These data were exported for analysis. Thanks to Scopus platform for allowing an export up to 2000 data at a time, unlike Web of Science (WoS), where a maximum of 500 data can be exported per time. Besides, Scopus also allows scholars to export data to different file formats such as BibTeX, CSV, Plain Text, RIS formats, etc. In this study, data were exported in BibTex format, which is allowable for importing into biblioshiny for bibliometrix tools (Aria & Cuccurullo, 2017 ).

Bibliometric analysis and software package

This study employed the use of bibliometrix R-package software, an open-source software that provides a set of tools for conducting quantitative research in bibliometrics. R-package was developed by Aria and Cuccurullo and written in the R language (Aria & Cuccurullo, 2017 ). It has the main algorithms for conducting statistical and science mapping analysis. The recent versions of bibliometrix R-package (i.e., 2.0 upwards) contains a web interface app (Biblioshiny) introduced to aid users without coding skills to conduct bibliometric analysis. Biblioshiny interface allows for data importing from Scopus or Web of Science databases in either BibTex, CSV, or Plain Text format. It is also possible to filter data in biblioshiny. Our study leveraged these opportunities inherent in biblioshiny for bibliometrix to import data from Scopus in BibTex format. The study analysis is presented in the result section.

Data synthesis

Table  2 presents the summary information of the dataset. For example, the table revealed the numbers of document types in the data collected. Conference papers ( n =497) are the highest number of the document type. Next is article papers ( n =477), then book chapters ( n =107). Other document types such as Notes, reviews, editorial, and short surveys accounted for the remaining 47.

Data synthesis indicating primary information and summary of the dataset

As used in this study, author’s keywords (DE) refer to a specific list of keywords authors of a publication have listed (usually less than ten) to describe what their study dwelt upon as used in the full-text. In contrast, keyword plus (ID) refer to extended keywords and phrases generated by Scopus system, which consist of keywords from the references cited by authors of a publication (Tripathi, Kumar, Sonker, & Babbar, 2018 ). In addition, authors per document refer to the mean number of authors per document, while co-author per document is the mean number of authors’ appearances per document—both authors per document and co-author per document measure authors’ collaboration.

Results and discussions

Results and discussion of findings are presented in this section to reflect (i) growth and trends of smart learning environment research in terms of publication output, distribution, source, and citations; (ii) prolific scholars, affiliations, and social networks; (iii) thematic focus of the field of smart learning environments.

Growth and trends of smart learning environments research

In this section, we begin by presenting the annual scientific production of articles in the field of smart learning environments. As shown in Table  3 , research in smart learning environments seems to commence in 2002 with the work of Sosteric and Hesemeier ( 2002 ) being the first and only article recorded in that year. Analysis from the bibliometrix R package shows that the field of smart learning environment has a 33.63% annual growth rate of scientific production from 2002 to mid-2020 (see Fig.  1 ). In 2015, 72 articles were recorded, which indicates the beginning of the impressive growth of publications in the field. This growth became drastic in 2016, where 138 articles were published. In 2019, 288 articles were published, which makes it the highest publication per year recorded so far. Since the field of smart learning environments is still emerging, it is expected, as revealed from the outcome of the analysis, that the scientific contribution would keep growing yearly.

Articles production per year

Annual scientific growth of smart learning environments: A compound annual growth rate computed by R- package, a geometric progression ratio with a constant scientific production rate over a period

Regarding the number of citations of smart learning environment publications, Table ​ Table4 4 presents the average citation per year. This result shows the amount of influence the publication has on the field per year. The result shows that the only single publication in 2002, which appears to be the beginning of the field, received an average number of 3.1 citations. This implies that the authors’— (Sosteric & Hesemeier, 2002 )—work had a good impact in the field of smart learning environments. There was a dwindling of the number of citations between 2003 and 2009. However, the average citations per year grew to 10.2 in 2010, which is the highest citations recorded so far. Surprisingly, this number dropped sharply to 0.6 in 2011 and 0.7 in 2013. The reason for this fall in the citation in both years was not evident to authors; however, they can be considered as outliers. Besides, it can be seen from Table ​ Table3 3 that the annual scientific production in both years did not rise so much, which may have caused the decline in the annual citation for that year.

Average citation per year

Relevant sources and documents of smart learning environment publications

In Fig.  2 , the result of the top 20 most relevant sources focused on publishing articles on the smart learning environments is presented. This result is based on the data from Scopus retrieved in June 2020. It is shown that lecture notes on educational technology remain the topmost relevant source. Other relevant sources include Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and that of Bioinformatics), Smart Innovation Systems and Technology, and Association for Computing Machinery (ACM) International Conference Proceeding series. Aside from these sources, dedicated journals shown by the analysis include Computers and Education, Educational Technology and Society, and Smart Learning Environments.

Distribution of articles by relevant sources from 2002 to mid-2020. (Based on this study’s Scopus dataset, lecture noted in educational technology remains the top source for smart learning environment publications)

Among these top 20 relevant sources, further investigation (see Fig.  3 ) shows that “Computers and Education” is the most locally cited source with 1013 documents. Next, most locally cited source is the “Smart Learning Environment”—a fully open access journal initiated in 2014; published by Springer, and dedicated to providing opportunities for dialogue on the need for reform and innovative use of emerging technologies and pedagogy towards advancing learning and teaching in the twenty-first century (Spector, 2016 ). The smart learning environment has a total of 622 documents based on the dataset. Closely following in the list of most local cited resources is “Computers in Human Behaviour”, which has 613 documents.

Relevant publishing outlets with most local citations: Computers & Education , Smart Learning Environment , and Computers in Human Behaviour s stands out

Regarding the relevant document recorded in the field of smart learning environments, this study investigated the global and local citation of publications. Global citation measures the number of citations a document has received from the entire database, in this case, the Scopus database. The global citation also measures the impact of a document, which in most cases, could receive its larger number of citations from other disciplines. On the other hand, local citation measures the number of citations a document has received from documents included in the analysed data. The local citation also measures the impact of a document in the analysed collections (Aria & Cuccurullo, 2020 ). In other words, global citation considers citations from a global perspective in terms of disciplines, while local citation focuses only on citations within a discipline under study. Research has shown that aside from the scientific productivity counts, the number of citations for a publication also forms indices for ascertaining its significance and scholarly impact (Grant, Cottrell, Cluzeau, & Fawcett, 2000 ; Waheed et al., 2018 ). To this end, the analysis shows that the most globally cited paper between 2002 and mid-2020 came from the article published by Baker, D'Mello, Rodrigo, and Graesser ( 2010 ) with total global citations of 400. This authors’ work focused on the use of three different computer-based learning environments to teach students and, thereafter, investigated the incidence, persistence, and impact of their cognitive-affective states (Baker et al., 2010 ). In addition, the result shows 20 most cited documents from the study dataset (see Table  5 ). In the analysis, authors of Biblioshiny for Bibliometrix had written the algorithm to consider the local citation in order to determine the impact of documents within a dicipline. This study revealed that the work of Kinshuk, Chen N. S, Cheng I.L., and Chew S.W. published in 2016 top the list with local citation of 38 and global citation of 43. Suprisingly, Baker et al. ( 2010 ) that received massive global citations failed to show up among the top 20 most locally cited documents based on the dataset analysed. Out of the 1081 data collected in this study, Baker et al. ( 2010 ) was ranked 22 in the list of most cited documents with a total number of local citations of 4 and a total number of global citations of 400. The discrepancy in the number of local and global citations cannot be unconnected to the widely focused nature of these authors’ work—computerized learning environments—rather than the field of smart learning environments, which form a subset of their work.

Top twenty most cited references based on number of local citations from the collection dataset

It is interesting to note also that a few studies in Table ​ Table5 5 received more local citations than global citations, as seen in the case of (Toivonen et al., 2018 ) and (Molina-Carmona & Villagr-Arnedo, 2018 ). One may think that the reverse should be the case. However, while authors cannot specifically give reasons for such a scenario, it might be the case of self-citations where these authors cited their study severally and published their works within the field of smart learning environments.

Scientific publication production by region/countries

The study also conducted an analysis of scientific production (i.e., amount of publications) and contribution to the field of smart learning environments across regions/countries. The result demonstrates that the United States has the highest publication count from North America and closely followed by China from the Asia region. From Europe, the analysis shows that countries such as Spain, Germany, Greece, Finland, Italy, Netherlands, Turkey, and the Czech Republic contribute substantively to the field of smart learning environments. Australia is actively contributing to the field of smart learning environments from their region. However, in the of Africa region, the result shows that a few countries such as South Africa, Tunisia, Nigeria, Morocco, Ghana, and Tanzania are making some contributions to the field smart learning environments.

Further analysis shows the first 20 countries with total and average citations. The United States remains the top country, followed by China. However, surprisingly, Macedonia that seems invisible among the countries in terms of publication counts, became the third-ranked country in total citations and average citations of 188 and 62.7, respectively. This implies that although Macedonia may not have produced plenty of scientific articles in smart learning environments, the few published ones have a huge impact. Furthermore, as presented in Table  6 , Germany and Finland are also among the top countries whose contributions in the field have a significant influence.

Top twenty most cited countries in the field of smart learning environment

Prolific scholars, institutions, and collaboration network

Prolific scholars in the field of smart learning environments.

Results from the top twenty most prolific scholars in the field of smart learning environments from 2002 to June 2020 based on the dataset are presented in Fig.  4 . These scholars have shown consistency by contributing to the research body in this field. The result revealed that Arthur C. Graesser from the United States had produced a total of 12 documents and earned the highest citation counts of 618. He also has the highest h-index, which suggests that Graesser remains the most impactful author in the field of smart learning environments. Graesser’s first article was published in 2007 with total citations per year of 4.6. Although our result shows that Graesser has no publication yet in 2020, however, he has consistently published in this field between 2010 to 2014. The second most prolific scholar in this field is Jose Aguilar from Colombia. Aguilar has an h-index of 6 and a total of 20 publications. Aguilar began publishing in the field of smart learning environments in 2016, where he had six publications and consistently published 5, 6, 2, and 1 papers in 2017, 2018, 2019, and 2020 respectively. Similarly, the result shows that Menno D.T. de Jong from the Netherlands, Hiroaki Ogata from Japan, and Kinshuk from the United States have h-index of 6, 5, and 4 respectively based on our dataset; hence they have immensely impacted the field of smart learning environments. Other great scholars in this field and their scientific productions are shown in Fig. ​ Fig.4 4 .

Top 20 authors productivity over the years: the line represents an authors timeline; bubbles size is proportional to the number of documents produced by an author per year; the color intensity of the bubble is proportional to the total number of citations per year; the first bubble on the line indicates when the author began to publish in the field; the bigger the bubble, the higher the number of articles published an author per year; bubbles with deeper color intensity indicates higher citation counts

A more visualized representation of prolific scholars vis-à-vis their countries and specific area of interest in the field of smart learning environments is shown in Fig.  5 . This figure is a three-field plot of article contributions by countries, authors, and themes within the field of smart learning environments. The left-most column represents active countries, the middle column shows scholars’ names contributing from those countries, and the rightmost column represents the most used keywords by authors. The number of occurrences of these keywords forms what we refer to as ‘themes’ in this study. Note the height of the boxes and the thickness of the connecting lines. On the side of countries, China has more authors’ affiliations, with 120 authors connected to the country. Although our result revealed earlier that the United States is first in terms of scientific production and citation counts, they came second in authors’ affiliation. In that order, Japan has the next higher volume of authors, followed by Tunisia and Canada. Observing the thickness of the line leading from the countries to authors, we can see that Ronghuai Huang and Gwo-Jen Hwang remains the giant contributors from China. Similarly, Arthur C. Graesser and Xiaoqiang Hu are the main authors contributing to the field of smart learning environments from the United States. In Japan, Hiroaki Ogata, Kousuke Mouri, and Noriko Uosaki remain the prolific writers.

A three-field plot of countries, authors, and themes of smart learning environments: The emphasis is placed on the height of each box and thickness of the connecting lines; the taller the box, the more significant; and the thicker the lines’ correlation, the more information or volume of work was produced

In addition, the aspect of learning analytic attracted more interest as the results show that 73 articles in learning analytics have emerged from authors such as Kinshuk, Hiroaki Ogata, and Kousuke Mouri, leading in that direction. Besides, the smart learning environment field also received interest and publications from Kinshuk and Ronghuai Huang as leading authors.

Institutions, co-authorship, and collaboration network

Regarding institutions and authors’ affiliations, contributing to the smart learning environment, the study investigated the publication output from the top 20 institutions. The result shows that Beijing Normal University, China tops with 37 documents. Next is the University of Memphis, in the United States, with a document count of 24. Athabasca University, Universidad De Los Andes, University of Hradec Kralove, the University of Twente, and Bradley University all belong to the top 20 institutions, with document numbers 22, 21, 20, 17, and 15, respectively (see Table  7 ).

Most relevant institutions in the field of smart learning environment

Regarding co-authorship and social collaboration analysis, the study explored the social structure component of the bibliometrix R-package (Aria & Cuccurullo, 2017 ) provided in the biblioshiny user interface (UI). According to scholars, the social network of actors within a field delineates the relationship between two or more individuals, institutions, or countries with regards to collaborations (Prell, Hubacek, & Reed, 2009 ; Song et al., 2019 ). These relationships are presented in a network where nodes represent actors, and links connecting the nodes represent the relationships. In this study, we present the collaboration network between authors, as shown in Fig.  6 , and the institution’s collaboration network, as shown in Fig.  7 . The result shows that the big names already mentioned as prolific scholars in the field, such as Kinshuk, Huang, Graesser, Ogata, De Jong, and Aguilar are having a well-established collaboration network.

Mapping of authors’ collaboration network; Authors’ names are written in the boxes; the bigger the box, the wider the author’s collaboration network; also, there exist networks within a network, e.g., Fathi Essalmi and Mohamed Jemni all connected to Kinshuk

Mapping of institutions collaboration and social networks: clearly, institutions with a bigger network of collaborations are boldened while those with a few networks or none are smaller

Similarly, institutions such as Beijing Normal University in China and the University of Twente in the Netherlands are seen to have created a big network of collaborations with other universities. For example, the Beijing Normal University has Arizona State University, Athabasca University, University of North Texas, Hong Kong Institute of Education, and Hangzhou Normal University in its network of collaborations. However, a few other universities are shown to have little or no collaboration network. Although these institutions are actively contributing to the research field of smart learning environments, they have not established collaborations with other institutions to expand their social network in the field. For example, Central China Normal University in China, the Graz University of Technology in Austria, the University of Eastern Finland in Finland, Bradley University in the United States, etcetera, are in isolation with no collaboration network.

Thematic focus of the field of smart learning environments

This section investigates the themes that dominate the research landscape of smart learning environments and areas that scholars have focused on over the years. Besides, the study also tries to gain insight into whether there is a shift in the topic of discussion among scholars within the field. We first began by analysing authors’ keywords and their frequency of occurrences. Next, we carried out an analysis of keywords dynamics, trending topics, co-occurrence network, and thematic areas of the field.

Keywords analysis, co-occurrence network, and trend topics

Analysis of keywords used by authors in publications is an essential tool for investigating trending topics and scholars focus in the field (Song et al., 2019 ). This analysis is so because publication keywords help to identify the topic and focus of that publication quickly. The word-cloud in Fig.  8 shows frequently used keywords in smart learning environments publications.

A Visualized Word-cloud of frequently used keywords in the smart learning environment field: these are among the highest number of repetitive keywords within the field

Specifically, Fig. ​ Fig.9 9 is a visualized word dynamics of the authors’ most used keywords. As shown in the figure, most of these keywords began to appear in the research landscape around 2010 and continued to grow afterward. While a few of them, such as “ smart learning environment ” and “ smart learning environments ,” began to witness a rapid growth after 2011, learning analytics had a negative trend.

Authors’ keywords dynamic view over time: it shows the growth of keywords ; learning analytic has grown till 2018 and began to decline thereafter; however, higher education, personalized learning, internet of thing, and blockchain are keywords that show upward growth as of 2020

However , learning analytics became one of the most used keywords from 2013 and grew very fast until 2018. This finding signified that learning analytics became the most discussed topic as an aspect of smart learning environments among scholars within those years. Notably, between 2018 and mid-2020, when this study was conducted, the use of these keywords began to nosedive. However, frequently used keywords such as higher education, online learning, smart education, adaptive learning, and personalized learning existed from around 2004 but began to rise after 2010. Between 2010 and 2020, keywords such as blockchain and internet of things emerged and continued to grow (see Fig. ​ Fig.9). 9 ). This finding suggests that the field of smart learning environment will continue to be researched around these prevailing aspects.

In addition, this study investigated the keywords co-occurrence network (KCN) in order to gain further insight into the trends in the field of smart learning environments. The KCN analysis presents the link between keywords in literature, which gives insight into the field’s knowledge structure (Esfahani et al., 2019 ). Therefore, our result shows that beyond identifying frequent keywords, as shown in the word-cloud (Fig. ​ (Fig.8), 8 ), KCN revealed the connections between them (see Fig.  10 ). Notably, some keywords seem to have a greater impact on a network. For example, a close examination of these keywords from its color code suggests that a bigger keyword represented by their width are cohesively connected to other smaller keywords. For instance, Education connects to digital storytelling, blockchain, IoT, ICT, and learning. Similarly, a keyword smart learning environment is closely connected to adaptive learning and learning management system.

Co-occurrence network of keywords: the thicker line indicates a strong association between those keywords; thinner lines depict weak association, and keywords without connecting lines indicate that no relationship has been established

Trending topics and thematic analysis of the field of smart learning environment

Furthermore, an analysis of the trending topic based on the author’s keywords from the dataset was conducted. While conducting the analysis, the following parameters were configured; timespan was set at 2011 to 2020, word minimum frequency was set to 5, number of words per year was set to 5, and word label size was also set to 5.

Article keywords, which authors define, are usually connected to such publication content and are sufficient to derive topical aspects of a field (Song et al., 2019 ). This analysis gives further insight into the trending topics in terms of keywords occurrences in smart learning literature over the years. Although many authors’ keywords are shown in the word-cloud (Fig. ​ (Fig.8), 8 ), the analysis in Fig. ​ Fig.11 11 presents the hierarchical arrangement of topics in smart learning environments discussed by scholars per year. These topics could relate to the field of smart learning environments in many ways. For instance, in 2016, inquiry learning was the most discussed topic, and it is a pedagogical domain of smart learning environments. Similarly, in 2017, smart learning was the leading topic, which is a key concept of smart learning environment; in 2018, learning analytics was top on the list, which also formed another critical domain of smart learning environment. The result also shows that as at the time of conducting this analysis, deep learning remains the trending topic in 2020.

Trending topics between 2011 to 2020

Another analysis conducted in this study is the thematic map of smart learning environments. The aim of conducting a thematic map is to gain insight into the field’s current status and what its future sustainability holds. This analysis is useful in providing knowledge to researchers and stakeholders regarding the potentials of future research development of thematic areas within a field.

The thematic analysis takes clusters of authors’ keywords and their interconnections to obtain themes. These themes are characterized by properties (density and centrality). The density is represented in the vertical axis, while centrality takes the horizontal axis. Centrality is the degree of correlation among different topics; density measures the cohesiveness among the nodes (Esfahani et al., 2019 ). These two properties measure whether certain topics are well developed or not, important or not. The higher the number of relations a node has with others in the thematic network, the higher the centrality and importance, and it lies within the essential position in the network. Similarly, cohesiveness among a node, which represents the density of a research field delineates its capability to develop and sustain itself. In Fig. ​ Fig.12, 12 , we provide the thematic map of the field of a smart learning environment, which is basically divided into four quadrants (Q1 to Q4).

Thematic map: Q1 contains the main theme, Q2 contains highly developed and specialized themes building ties with the leading theme; Q3 contains disappearing or emerging themes; Q4 consists of foundational and transversal themes

The upper right quadrant (Q1) represents driving themes, the lower right quadrant (Q4) is underlying themes, the upper left quadrant (Q2) is the very specialized themes, and the lower left quadrant (Q3) is emerging or disappearing themes. Notably from the figure, a theme such as “smart learning,” sandwiched between Q1 and Q4, is well developed and capable of structuring the research field. In other words, smart learning remains the leading theme within the field. Themes such as “education” and “e-learning” seen in Q4 are the basics and are very important for the field’s development. Themes in Q2 have developed internal bonds but still of marginal contribution to the development of the field of smart learning. This finding suggests that themes in Q2 such as storytelling, virtual reality, critical thinking, and serious games, are potential topics that need to be more connected to smart learning. Scholars in this field may explore these pedagogical tools (storytelling and serious games) and technological approach (virtual reality) to provide smart learning environments for a twenty-first-century learning experience.

The theme in Q3, “learning analytic,” appears to be emerging but transverses Q4, indicating that some of its components are basic and necessary for developing the field of smart learning environments. The thematic analysis suggests that more efforts are needed to develop themes such as “digital storytelling” and its associated components such as virtual reality, critical thinking, and serious games, to establish more ties with “smart learning”. This effort is necessary because digital storytelling, an established field, can significantly contribute to the smart learning environment’s structure, future, and sustainable development.

Conclusions

This study has tried to provide an extensive review of scientific publications in the field of smart learning environment over time using bibliometric analysis. The study investigated the themes of smart learning in the publications; recognized prolific scholars and their contributions; explored social networks and collaborations across institutions, countries, and regions over time, and presented the thematic analysis of the field of smart learning environments by showing its current status regarding the themes, and future prospects. A total of 1081 documents were retrieved from the Scopus database for this study. This work makes a number of prominent contributions to the research body. First, the study revealed that the first paper on smart learning environments was published in 2002, which perhaps signifies the beginning of the field of the smart learning environment. Relevant publishing outlets were identified in this study. Foremost among the publishing sources as revealed by the study is the “Computers & Education.” This result provides an important guide to scholars regarding the publishing outlet that is suitable for their research papers.

Additionally, an investigation into relevant articles published in the field revealed that the work of Kinshuk et al. ( 2016 ) stands out; these authors work mainly focused on the transformation of technology-enhanced learning into smart learning environments. Perhaps, their work sets the stage for discussions on the features and characteristics of smart learning environments from the technology and pedagogy perspectives. Similarly, our result delineates that the United States has the highest number of scientific productions in the field of smart learning environments over the years. That suggests that the United States remained the most relevant country in the field of smart learning environments. Regarding institutions’ contributions and relevance, Beijing Normal University in China tops the list. In the aspect of prolific scholars making an immense contribution to the field of smart learning environments, Arthur C. Graesser from the United States tops the list with an h-index of 8. Besides, scholars such as Kinshuk, Graesser, Ogata, De Jong, and Aguilar have established a wide range of collaboration networks.

Furthermore, the study revealed that the field of smart learning environments is recently evolving with the emerging and growing aspects such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning”. The thematic analysis results show that themes such as “digital storytelling” are emerging and connected to smart learning environments. However, this theme and its associated components, such as virtual reality, critical thinking, and serious games, needs to be further developed to establish more ties with “smart learning”. The study further showed that in the mid-year of 2020, “deep learning” remains the trending topic. It is interesting to discover that between 2017 and 2020, newer topics connected to artificial intelligence (AI) such as learning analytics, blockchain, and deep learning, have emerged and grown to become research hotspots in smart learning environments. These findings underscore the importance of deepening further studies to leverage AI in future designs of smart learning environments. As part of our conclusion, some suggestions for future research in the field of smart learning environments are highlighted in this study.

• It could be essential to develop more extensive research collaborations between scholars and institutions, thereby creating a more global impact on smart learning environments’ potentials for an enhanced learning experience.
• It is suggested that scholars invest more effort in learning analytics, machine learning, and deep learning, as the study shows that they are future research topics in smart learning environments.
• More effort into researching digital storytelling, serious games, virtual reality, and critical thinking by educational technologists and designers of smart learning environments is suggested. This study has shown that there are potentials to adopt these strategies in developing twenty-first-century learning.

Study limitations

This study has some limitations. Majorly, the study weakness is about the sample data collection. The study encountered a technical limitation in terms of the software used to conduct the analysis, where the merging of data from different databases was not possible at the time the study was conducted. The sample in this study was collected from the Scopus database, which may result in missing out relevant data. Collecting sample data from multiple independent databases would certainly improve the study in a significant way. In addition, the search keywords used in querying the database could be improved to consist more relevant keywords. This limitation should motivate future work where scholars could explore ways of collecting data from multiple databases with expanded keywords for a more in-depth analysis.

In sum, we conclude that this study hopes its findings will provide insight to researchers, specifically, the young scholars in smart learning environments regarding the research landscape and future research hotspots. For example, young researchers who are beginning to research in the field can quickly identify top articles, prolific authors, and research hotspots in the field of smart learning environments. In addition, the study shows emerging topics in the field of smart learning environments, which needs to be further developed to connect to the objective of smart learning. Findings from this study provide a quick overview of the output in this field over the years and relevant pointer to the future direction in the field of smart learning environments.

Acknowledgements

Not applicable.

Abbreviations

Authors’ contributions

The first author was the lead researcher and contributed to the aspect of research design, data collection, data analysis, revising the manuscript to improve different sections, and prepared the paper for submission to the journal. The second author contributed in terms of conceptualizing the key concept of the idea, data analysis, revising the manuscript severally to improve the research, and provided support during the revision of the paper. The third and fourth authors contributed in terms of structuring the analysis, and reviewing the entire work to improve different aspects, provided high-level comments to strengthen the paper. The authors read and approved the final manuscript.

Availability of data and materials

Competing interests.

The authors declare that they have no competing interests.

1 https://bibliometrix.org/

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Contributor Information

Friday Joseph Agbo, Email: if.feu@ayadirf .

Solomon Sunday Oyelere, Email: [email protected] .

Jarkko Suhonen, Email: [email protected] .

Markku Tukiainen, Email: [email protected] .

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• Published: 15 January 2021

Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis

• Friday Joseph Agbo   ORCID: orcid.org/0000-0002-9171-7175 1 ,
• Solomon Sunday Oyelere 2 ,
• Jarkko Suhonen 1 &
• Markku Tukiainen 1  

Smart Learning Environments volume  8 , Article number:  1 ( 2021 ) Cite this article

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

The evolution of learning and teaching methods from the traditional classroom learning environment to a technology-enhanced learning environment positively impacts education (Cárdenas-Robledo & Peña-Ayal, 2018 ; McIntosh, Herman, Sanford, McGraw, & Florence, 2004 ). This transition is even more relevant nowadays due to unforeseen circumstances that create an emergency on the world’s education, for example, where formal learning is not possible due to closure of schools as experienced in the recent COVID-19 pandemic (Atchison et al., 2020 ). As a result of this pandemic and to prevent the spread of the disease, many countries adopted online distance learning as an alternative teaching model (Reimers & Schleicher, 2020 ). This situation underscores the importance of developing a flexible, personalized, and adaptive learning environment to facilitate learning and teaching anytime, anywhere without physical contact and limited human interventions.

Research has shown that smart learning environments (SLE) can provide a twenty-first-century learning environment powered by advanced technology (Kim, Cho, & Lee, 2012 ; Laine & Joy, 2009 ), pedagogy (Tomczyk et al., 2019 ), and creative strategies (Harris, Dousay, Hall, Srinivasan, & Srinivasan, 2020 ). Thus, SLE promises to provide the future learning ecosystem by leveraging advanced learner models and evolving new technology. Smart learning environments refers to ubiquitous, context-aware, personalized, and intelligent system capable of providing a high level of motivation, engagement, and intelligent feedback for a better learning experience (Agbo et al., 2019 ; Hwang, 2014 ). The emerging field of smart learning environments began to gain scholars’ attention in recent times. The increasing growth of the field creates the opportunity to investigate the smart learning environments trends in the literature and how its discussion among scholars has progressed. A comprehensive review of literature in smart learning environments is very important. First, it will provide overview of the progress made by scholars and their status. Second, it will reveal critical information that can guide researchers in making decision regarding areas to focus their future research (field hotspots); and which publication outlet is suitable for publication. To this end, this study examines the research landscape of the smart learning environment to gain a comprehensive understanding of the research activities from a multidisciplinary perspective, trends, and possible future direction of the field.

Wang et al. ( 2020 ) recently conducted a related study that examined the research trend, status in the field of smart learning within China from 2012 to 2019. These authors, (Wang et al., 2020 ) were interested in knowing when research in smart learning began in China, its trend, and scholars’ publication contributions. The problem with this paper is that it is limited to a country and date bound. While our study derives motivation from Wang et al. ( 2020 ), it takes a different approach by conducting a comprehensive and all-encompassing study that is not limited to specific date ranges, regions, or countries. Besides, this study is focused on the science mapping of literature from the Scopus database by using the Bibliometric approach (Esfahani, Tavasoli, & Jabbarzadeh, 2019 ; Gilani, Salimi, Jouyandeh, Tavasoli, & Wong, 2019 ). Science mapping technic with Bibliometrix R-package is a useful approach to performing the Bibliometric analysis of scientific publications (Aria & Cuccurullo, 2017 ). A bibliometric study has been acclaimed to provide a useful tool for analysing the evolution of discipline based on its intellectual contributions, social, and conceptual structure (Zupic & Čater, 2015 ). Besides, many similar studies have applied bibliometric analysis to present an overview of specific field research. Among these studies, it is worthy of mentioning some recent and related areas such as research landscape of learning analytics (Waheed, Hassan, Aljohani, & Wasif, 2018 ), augmented reality research (Arici, Yildirim, Caliklar, & Yilmaz, 2019 ), multimedia learning research (Li, Antonenko, & Wang, 2019 ), and research on classroom dialogue (Song et al., 2019 ). These studies identified most outstanding publications, publication outlets, prolific scholars, research topics, and trends in the respective fields.

Research objectives

This study aims to present a comprehensive review of the smart learning environment; hence, a bibliometric analysis is appropriate. To the best of our knowledge, no extensive bibliometric study of literature on smart learning environments has been conducted. This study is the first to conduct a bibliometric analysis of the field with a specific objective to examine the trend of smart learning environments over time; investigate the themes of smart learning in the publications; recognize prolific scholars and their contribution in the field of the smart learning environment; explore publication networks and collaborations across institutions, countries, and regions over time. Additionally, the study intends to identify any shift in the smart learning environment field’s boundaries from a large body of information in extant research.

The outcome of this study will provide useful knowledge for young scholars, mostly the young ones who are just starting to research in the field of smart learning environments. For example, young researchers can quickly identify top articles in terms of the number of citations, prolific authors, and research hotspots. Besides information such as trending topics and thematic future direction of smart learning environments can stimulate young researchers’ decision in terms of research interest. The main research question that this study seeks to answer is: how research in the field of smart learning environments has progressed over the years in terms of scientific productions, thematic breakthroughs, scholars’ contributions, and future thematic direction?

In this study, a bibliometric mapping analysis was conducted. Bibliometric mapping is recently gaining more grounds in different disciplines (Aria & Cuccurullo, 2017 ; Arici et al., 2019 ; Song et al., 2019 ). Perhaps, the suitability of bibliometric for science mapping may have caused this extending acceptance among scholars (Aria & Cuccurullo, 2017 ). The entire procedure for conducting bibliometric mapping analysis in this study including data collection, screening, extraction, and synthesis are presented in this section.

Literature search and data collection

First, we commenced by conducting a document search on the Scopus database. The search string consists of a combination of compound keywords concatenated with the OR operator. The first search field contained the keywords “smart learning environment” to search “All fields,” while the second search field contained keywords such as adaptive, context*, personalized, and intelligent. These additional keywords in the second line of the search field were selected because they are mostly used to define the characteristic features of smart learning environments (Hwang, 2014 ). Besides, these keywords have been associated with smart learning. For instance, Molina-Carmona and Villagr-Arnedo ( 2018 ) in their study entitled “smart learning”, emphasized keywords such as “personalized learning”, “adaptive learning, situation or context-aware learning as key orchestrates smart learning environments. The initial query without any filtering returned 1212 document results. The search and retrieval of the data were conducted on June 19, 2020. These results were later filtered to exclude some irrelevant items based on our inclusion and exclusion criteria. The inclusion and exclusion criteria are presented in Table  1 . The search string combinations, operators, and filtering using the criteria explained in Table 1 is shown below.

(ALL(“smart learning environment”) OR TITLE-ABS-KEY(“adaptive context* personalized intelligent”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO(DOCTYPE, “ar”) OR LIMIT-TO(DOCTYPE, “cp”) OR LIMIT-TO(DOCTYPE,"ch”)) AND (EXCLUDE(PUBSTAGE, “aip”)) AND (EXCLUDE(SRCTYPE, “d”)) .

As the database was limited to Scopus, authors do not claim that an exhaustive list of data was acquired. The possibility of missing out on data from other databases such as Web of Science, PubMed, ERIC, etcetera could be minimal if a compatible formatting standard that allows for merging data generated from independent databases exists. Unfortunately, the bibliometrix R-package software Footnote 1 used in this study does not currently support this ambition. However, Scopus covers a large number of articles and provides higher records in terms of citations (Heradio et al., 2016 ; Shen & Ho, 2020 ). Consequently, we claim that sufficient data to outline the scientific landscape, research hotspots, and other analysis conducted in this study was retrieved.

Data extraction, loading, and conversion

In total, 1081 data were collected after refining based on the inclusion and exclusion criteria shown in Table 1 . These data were exported for analysis. Thanks to Scopus platform for allowing an export up to 2000 data at a time, unlike Web of Science (WoS), where a maximum of 500 data can be exported per time. Besides, Scopus also allows scholars to export data to different file formats such as BibTeX, CSV, Plain Text, RIS formats, etc. In this study, data were exported in BibTex format, which is allowable for importing into biblioshiny for bibliometrix tools (Aria & Cuccurullo, 2017 ).

Bibliometric analysis and software package

This study employed the use of bibliometrix R-package software, an open-source software that provides a set of tools for conducting quantitative research in bibliometrics. R-package was developed by Aria and Cuccurullo and written in the R language (Aria & Cuccurullo, 2017 ). It has the main algorithms for conducting statistical and science mapping analysis. The recent versions of bibliometrix R-package (i.e., 2.0 upwards) contains a web interface app (Biblioshiny) introduced to aid users without coding skills to conduct bibliometric analysis. Biblioshiny interface allows for data importing from Scopus or Web of Science databases in either BibTex, CSV, or Plain Text format. It is also possible to filter data in biblioshiny. Our study leveraged these opportunities inherent in biblioshiny for bibliometrix to import data from Scopus in BibTex format. The study analysis is presented in the result section.

Data synthesis

Table  2 presents the summary information of the dataset. For example, the table revealed the numbers of document types in the data collected. Conference papers ( n =497) are the highest number of the document type. Next is article papers ( n =477), then book chapters ( n =107). Other document types such as Notes, reviews, editorial, and short surveys accounted for the remaining 47.

As used in this study, author’s keywords (DE) refer to a specific list of keywords authors of a publication have listed (usually less than ten) to describe what their study dwelt upon as used in the full-text. In contrast, keyword plus (ID) refer to extended keywords and phrases generated by Scopus system, which consist of keywords from the references cited by authors of a publication (Tripathi, Kumar, Sonker, & Babbar, 2018 ). In addition, authors per document refer to the mean number of authors per document, while co-author per document is the mean number of authors’ appearances per document—both authors per document and co-author per document measure authors’ collaboration.

Results and discussions

Results and discussion of findings are presented in this section to reflect (i) growth and trends of smart learning environment research in terms of publication output, distribution, source, and citations; (ii) prolific scholars, affiliations, and social networks; (iii) thematic focus of the field of smart learning environments.

Growth and trends of smart learning environments research

In this section, we begin by presenting the annual scientific production of articles in the field of smart learning environments. As shown in Table  3 , research in smart learning environments seems to commence in 2002 with the work of Sosteric and Hesemeier ( 2002 ) being the first and only article recorded in that year. Analysis from the bibliometrix R package shows that the field of smart learning environment has a 33.63% annual growth rate of scientific production from 2002 to mid-2020 (see Fig.  1 ). In 2015, 72 articles were recorded, which indicates the beginning of the impressive growth of publications in the field. This growth became drastic in 2016, where 138 articles were published. In 2019, 288 articles were published, which makes it the highest publication per year recorded so far. Since the field of smart learning environments is still emerging, it is expected, as revealed from the outcome of the analysis, that the scientific contribution would keep growing yearly.

Annual scientific growth of smart learning environments: A compound annual growth rate computed by R- package, a geometric progression ratio with a constant scientific production rate over a period

Regarding the number of citations of smart learning environment publications, Table 4 presents the average citation per year. This result shows the amount of influence the publication has on the field per year. The result shows that the only single publication in 2002, which appears to be the beginning of the field, received an average number of 3.1 citations. This implies that the authors’— (Sosteric & Hesemeier, 2002 )—work had a good impact in the field of smart learning environments. There was a dwindling of the number of citations between 2003 and 2009. However, the average citations per year grew to 10.2 in 2010, which is the highest citations recorded so far. Surprisingly, this number dropped sharply to 0.6 in 2011 and 0.7 in 2013. The reason for this fall in the citation in both years was not evident to authors; however, they can be considered as outliers. Besides, it can be seen from Table 3 that the annual scientific production in both years did not rise so much, which may have caused the decline in the annual citation for that year.

Relevant sources and documents of smart learning environment publications

In Fig.  2 , the result of the top 20 most relevant sources focused on publishing articles on the smart learning environments is presented. This result is based on the data from Scopus retrieved in June 2020. It is shown that lecture notes on educational technology remain the topmost relevant source. Other relevant sources include Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and that of Bioinformatics), Smart Innovation Systems and Technology, and Association for Computing Machinery (ACM) International Conference Proceeding series. Aside from these sources, dedicated journals shown by the analysis include Computers and Education, Educational Technology and Society, and Smart Learning Environments.

Distribution of articles by relevant sources from 2002 to mid-2020. (Based on this study’s Scopus dataset, lecture noted in educational technology remains the top source for smart learning environment publications)

Among these top 20 relevant sources, further investigation (see Fig.  3 ) shows that “Computers and Education” is the most locally cited source with 1013 documents. Next, most locally cited source is the “Smart Learning Environment”—a fully open access journal initiated in 2014; published by Springer, and dedicated to providing opportunities for dialogue on the need for reform and innovative use of emerging technologies and pedagogy towards advancing learning and teaching in the twenty-first century (Spector, 2016 ). The smart learning environment has a total of 622 documents based on the dataset. Closely following in the list of most local cited resources is “Computers in Human Behaviour”, which has 613 documents.

Relevant publishing outlets with most local citations: Computers & Education , Smart Learning Environment , and Computers in Human Behaviour s stands out

Regarding the relevant document recorded in the field of smart learning environments, this study investigated the global and local citation of publications. Global citation measures the number of citations a document has received from the entire database, in this case, the Scopus database. The global citation also measures the impact of a document, which in most cases, could receive its larger number of citations from other disciplines. On the other hand, local citation measures the number of citations a document has received from documents included in the analysed data. The local citation also measures the impact of a document in the analysed collections (Aria & Cuccurullo, 2020 ). In other words, global citation considers citations from a global perspective in terms of disciplines, while local citation focuses only on citations within a discipline under study. Research has shown that aside from the scientific productivity counts, the number of citations for a publication also forms indices for ascertaining its significance and scholarly impact (Grant, Cottrell, Cluzeau, & Fawcett, 2000 ; Waheed et al., 2018 ). To this end, the analysis shows that the most globally cited paper between 2002 and mid-2020 came from the article published by Baker, D'Mello, Rodrigo, and Graesser ( 2010 ) with total global citations of 400. This authors’ work focused on the use of three different computer-based learning environments to teach students and, thereafter, investigated the incidence, persistence, and impact of their cognitive-affective states (Baker et al., 2010 ). In addition, the result shows 20 most cited documents from the study dataset (see Table  5 ). In the analysis, authors of Biblioshiny for Bibliometrix had written the algorithm to consider the local citation in order to determine the impact of documents within a dicipline. This study revealed that the work of Kinshuk, Chen N. S, Cheng I.L., and Chew S.W. published in 2016 top the list with local citation of 38 and global citation of 43. Suprisingly, Baker et al. ( 2010 ) that received massive global citations failed to show up among the top 20 most locally cited documents based on the dataset analysed. Out of the 1081 data collected in this study, Baker et al. ( 2010 ) was ranked 22 in the list of most cited documents with a total number of local citations of 4 and a total number of global citations of 400. The discrepancy in the number of local and global citations cannot be unconnected to the widely focused nature of these authors’ work—computerized learning environments—rather than the field of smart learning environments, which form a subset of their work.

It is interesting to note also that a few studies in Table 5 received more local citations than global citations, as seen in the case of (Toivonen et al., 2018 ) and (Molina-Carmona & Villagr-Arnedo, 2018 ). One may think that the reverse should be the case. However, while authors cannot specifically give reasons for such a scenario, it might be the case of self-citations where these authors cited their study severally and published their works within the field of smart learning environments.

Scientific publication production by region/countries

The study also conducted an analysis of scientific production (i.e., amount of publications) and contribution to the field of smart learning environments across regions/countries. The result demonstrates that the United States has the highest publication count from North America and closely followed by China from the Asia region. From Europe, the analysis shows that countries such as Spain, Germany, Greece, Finland, Italy, Netherlands, Turkey, and the Czech Republic contribute substantively to the field of smart learning environments. Australia is actively contributing to the field of smart learning environments from their region. However, in the of Africa region, the result shows that a few countries such as South Africa, Tunisia, Nigeria, Morocco, Ghana, and Tanzania are making some contributions to the field smart learning environments.

Further analysis shows the first 20 countries with total and average citations. The United States remains the top country, followed by China. However, surprisingly, Macedonia that seems invisible among the countries in terms of publication counts, became the third-ranked country in total citations and average citations of 188 and 62.7, respectively. This implies that although Macedonia may not have produced plenty of scientific articles in smart learning environments, the few published ones have a huge impact. Furthermore, as presented in Table  6 , Germany and Finland are also among the top countries whose contributions in the field have a significant influence.

Prolific scholars, institutions, and collaboration network

Prolific scholars in the field of smart learning environments.

Results from the top twenty most prolific scholars in the field of smart learning environments from 2002 to June 2020 based on the dataset are presented in Fig.  4 . These scholars have shown consistency by contributing to the research body in this field. The result revealed that Arthur C. Graesser from the United States had produced a total of 12 documents and earned the highest citation counts of 618. He also has the highest h-index, which suggests that Graesser remains the most impactful author in the field of smart learning environments. Graesser’s first article was published in 2007 with total citations per year of 4.6. Although our result shows that Graesser has no publication yet in 2020, however, he has consistently published in this field between 2010 to 2014. The second most prolific scholar in this field is Jose Aguilar from Colombia. Aguilar has an h-index of 6 and a total of 20 publications. Aguilar began publishing in the field of smart learning environments in 2016, where he had six publications and consistently published 5, 6, 2, and 1 papers in 2017, 2018, 2019, and 2020 respectively. Similarly, the result shows that Menno D.T. de Jong from the Netherlands, Hiroaki Ogata from Japan, and Kinshuk from the United States have h-index of 6, 5, and 4 respectively based on our dataset; hence they have immensely impacted the field of smart learning environments. Other great scholars in this field and their scientific productions are shown in Fig. 4 .

Top 20 authors productivity over the years: the line represents an authors timeline; bubbles size is proportional to the number of documents produced by an author per year; the color intensity of the bubble is proportional to the total number of citations per year; the first bubble on the line indicates when the author began to publish in the field; the bigger the bubble, the higher the number of articles published an author per year; bubbles with deeper color intensity indicates higher citation counts

A more visualized representation of prolific scholars vis-à-vis their countries and specific area of interest in the field of smart learning environments is shown in Fig.  5 . This figure is a three-field plot of article contributions by countries, authors, and themes within the field of smart learning environments. The left-most column represents active countries, the middle column shows scholars’ names contributing from those countries, and the rightmost column represents the most used keywords by authors. The number of occurrences of these keywords forms what we refer to as ‘themes’ in this study. Note the height of the boxes and the thickness of the connecting lines. On the side of countries, China has more authors’ affiliations, with 120 authors connected to the country. Although our result revealed earlier that the United States is first in terms of scientific production and citation counts, they came second in authors’ affiliation. In that order, Japan has the next higher volume of authors, followed by Tunisia and Canada. Observing the thickness of the line leading from the countries to authors, we can see that Ronghuai Huang and Gwo-Jen Hwang remains the giant contributors from China. Similarly, Arthur C. Graesser and Xiaoqiang Hu are the main authors contributing to the field of smart learning environments from the United States. In Japan, Hiroaki Ogata, Kousuke Mouri, and Noriko Uosaki remain the prolific writers.

A three-field plot of countries, authors, and themes of smart learning environments: The emphasis is placed on the height of each box and thickness of the connecting lines; the taller the box, the more significant; and the thicker the lines’ correlation, the more information or volume of work was produced

In addition, the aspect of learning analytic attracted more interest as the results show that 73 articles in learning analytics have emerged from authors such as Kinshuk, Hiroaki Ogata, and Kousuke Mouri, leading in that direction. Besides, the smart learning environment field also received interest and publications from Kinshuk and Ronghuai Huang as leading authors.

Institutions, co-authorship, and collaboration network

Regarding institutions and authors’ affiliations, contributing to the smart learning environment, the study investigated the publication output from the top 20 institutions. The result shows that Beijing Normal University, China tops with 37 documents. Next is the University of Memphis, in the United States, with a document count of 24. Athabasca University, Universidad De Los Andes, University of Hradec Kralove, the University of Twente, and Bradley University all belong to the top 20 institutions, with document numbers 22, 21, 20, 17, and 15, respectively (see Table  7 ).

Regarding co-authorship and social collaboration analysis, the study explored the social structure component of the bibliometrix R-package (Aria & Cuccurullo, 2017 ) provided in the biblioshiny user interface (UI). According to scholars, the social network of actors within a field delineates the relationship between two or more individuals, institutions, or countries with regards to collaborations (Prell, Hubacek, & Reed, 2009 ; Song et al., 2019 ). These relationships are presented in a network where nodes represent actors, and links connecting the nodes represent the relationships. In this study, we present the collaboration network between authors, as shown in Fig.  6 , and the institution’s collaboration network, as shown in Fig.  7 . The result shows that the big names already mentioned as prolific scholars in the field, such as Kinshuk, Huang, Graesser, Ogata, De Jong, and Aguilar are having a well-established collaboration network.

Mapping of authors’ collaboration network; Authors’ names are written in the boxes; the bigger the box, the wider the author’s collaboration network; also, there exist networks within a network, e.g., Fathi Essalmi and Mohamed Jemni all connected to Kinshuk

Mapping of institutions collaboration and social networks: clearly, institutions with a bigger network of collaborations are boldened while those with a few networks or none are smaller

Similarly, institutions such as Beijing Normal University in China and the University of Twente in the Netherlands are seen to have created a big network of collaborations with other universities. For example, the Beijing Normal University has Arizona State University, Athabasca University, University of North Texas, Hong Kong Institute of Education, and Hangzhou Normal University in its network of collaborations. However, a few other universities are shown to have little or no collaboration network. Although these institutions are actively contributing to the research field of smart learning environments, they have not established collaborations with other institutions to expand their social network in the field. For example, Central China Normal University in China, the Graz University of Technology in Austria, the University of Eastern Finland in Finland, Bradley University in the United States, etcetera, are in isolation with no collaboration network.

Thematic focus of the field of smart learning environments

This section investigates the themes that dominate the research landscape of smart learning environments and areas that scholars have focused on over the years. Besides, the study also tries to gain insight into whether there is a shift in the topic of discussion among scholars within the field. We first began by analysing authors’ keywords and their frequency of occurrences. Next, we carried out an analysis of keywords dynamics, trending topics, co-occurrence network, and thematic areas of the field.

Keywords analysis, co-occurrence network, and trend topics

Analysis of keywords used by authors in publications is an essential tool for investigating trending topics and scholars focus in the field (Song et al., 2019 ). This analysis is so because publication keywords help to identify the topic and focus of that publication quickly. The word-cloud in Fig.  8 shows frequently used keywords in smart learning environments publications.

A Visualized Word-cloud of frequently used keywords in the smart learning environment field: these are among the highest number of repetitive keywords within the field

Specifically, Fig. 9 is a visualized word dynamics of the authors’ most used keywords. As shown in the figure, most of these keywords began to appear in the research landscape around 2010 and continued to grow afterward. While a few of them, such as “ smart learning environment ” and “ smart learning environments ,” began to witness a rapid growth after 2011, learning analytics had a negative trend.

Authors’ keywords dynamic view over time: it shows the growth of keywords ; learning analytic has grown till 2018 and began to decline thereafter; however, higher education, personalized learning, internet of thing, and blockchain are keywords that show upward growth as of 2020

However , learning analytics became one of the most used keywords from 2013 and grew very fast until 2018. This finding signified that learning analytics became the most discussed topic as an aspect of smart learning environments among scholars within those years. Notably, between 2018 and mid-2020, when this study was conducted, the use of these keywords began to nosedive. However, frequently used keywords such as higher education, online learning, smart education, adaptive learning, and personalized learning existed from around 2004 but began to rise after 2010. Between 2010 and 2020, keywords such as blockchain and internet of things emerged and continued to grow (see Fig. 9 ). This finding suggests that the field of smart learning environment will continue to be researched around these prevailing aspects.

In addition, this study investigated the keywords co-occurrence network (KCN) in order to gain further insight into the trends in the field of smart learning environments. The KCN analysis presents the link between keywords in literature, which gives insight into the field’s knowledge structure (Esfahani et al., 2019 ). Therefore, our result shows that beyond identifying frequent keywords, as shown in the word-cloud (Fig. 8 ), KCN revealed the connections between them (see Fig.  10 ). Notably, some keywords seem to have a greater impact on a network. For example, a close examination of these keywords from its color code suggests that a bigger keyword represented by their width are cohesively connected to other smaller keywords. For instance, Education connects to digital storytelling, blockchain, IoT, ICT, and learning. Similarly, a keyword smart learning environment is closely connected to adaptive learning and learning management system.

Co-occurrence network of keywords: the thicker line indicates a strong association between those keywords; thinner lines depict weak association, and keywords without connecting lines indicate that no relationship has been established

Trending topics and thematic analysis of the field of smart learning environment

Furthermore, an analysis of the trending topic based on the author’s keywords from the dataset was conducted. While conducting the analysis, the following parameters were configured; timespan was set at 2011 to 2020, word minimum frequency was set to 5, number of words per year was set to 5, and word label size was also set to 5.

Article keywords, which authors define, are usually connected to such publication content and are sufficient to derive topical aspects of a field (Song et al., 2019 ). This analysis gives further insight into the trending topics in terms of keywords occurrences in smart learning literature over the years. Although many authors’ keywords are shown in the word-cloud (Fig. 8 ), the analysis in Fig. 11 presents the hierarchical arrangement of topics in smart learning environments discussed by scholars per year. These topics could relate to the field of smart learning environments in many ways. For instance, in 2016, inquiry learning was the most discussed topic, and it is a pedagogical domain of smart learning environments. Similarly, in 2017, smart learning was the leading topic, which is a key concept of smart learning environment; in 2018, learning analytics was top on the list, which also formed another critical domain of smart learning environment. The result also shows that as at the time of conducting this analysis, deep learning remains the trending topic in 2020.

Trending topics between 2011 to 2020

Another analysis conducted in this study is the thematic map of smart learning environments. The aim of conducting a thematic map is to gain insight into the field’s current status and what its future sustainability holds. This analysis is useful in providing knowledge to researchers and stakeholders regarding the potentials of future research development of thematic areas within a field.

The thematic analysis takes clusters of authors’ keywords and their interconnections to obtain themes. These themes are characterized by properties (density and centrality). The density is represented in the vertical axis, while centrality takes the horizontal axis. Centrality is the degree of correlation among different topics; density measures the cohesiveness among the nodes (Esfahani et al., 2019 ). These two properties measure whether certain topics are well developed or not, important or not. The higher the number of relations a node has with others in the thematic network, the higher the centrality and importance, and it lies within the essential position in the network. Similarly, cohesiveness among a node, which represents the density of a research field delineates its capability to develop and sustain itself. In Fig. 12 , we provide the thematic map of the field of a smart learning environment, which is basically divided into four quadrants (Q1 to Q4).

Thematic map: Q1 contains the main theme, Q2 contains highly developed and specialized themes building ties with the leading theme; Q3 contains disappearing or emerging themes; Q4 consists of foundational and transversal themes

The upper right quadrant (Q1) represents driving themes, the lower right quadrant (Q4) is underlying themes, the upper left quadrant (Q2) is the very specialized themes, and the lower left quadrant (Q3) is emerging or disappearing themes. Notably from the figure, a theme such as “smart learning,” sandwiched between Q1 and Q4, is well developed and capable of structuring the research field. In other words, smart learning remains the leading theme within the field. Themes such as “education” and “e-learning” seen in Q4 are the basics and are very important for the field’s development. Themes in Q2 have developed internal bonds but still of marginal contribution to the development of the field of smart learning. This finding suggests that themes in Q2 such as storytelling, virtual reality, critical thinking, and serious games, are potential topics that need to be more connected to smart learning. Scholars in this field may explore these pedagogical tools (storytelling and serious games) and technological approach (virtual reality) to provide smart learning environments for a twenty-first-century learning experience.

The theme in Q3, “learning analytic,” appears to be emerging but transverses Q4, indicating that some of its components are basic and necessary for developing the field of smart learning environments. The thematic analysis suggests that more efforts are needed to develop themes such as “digital storytelling” and its associated components such as virtual reality, critical thinking, and serious games, to establish more ties with “smart learning”. This effort is necessary because digital storytelling, an established field, can significantly contribute to the smart learning environment’s structure, future, and sustainable development.

Conclusions

This study has tried to provide an extensive review of scientific publications in the field of smart learning environment over time using bibliometric analysis. The study investigated the themes of smart learning in the publications; recognized prolific scholars and their contributions; explored social networks and collaborations across institutions, countries, and regions over time, and presented the thematic analysis of the field of smart learning environments by showing its current status regarding the themes, and future prospects. A total of 1081 documents were retrieved from the Scopus database for this study. This work makes a number of prominent contributions to the research body. First, the study revealed that the first paper on smart learning environments was published in 2002, which perhaps signifies the beginning of the field of the smart learning environment. Relevant publishing outlets were identified in this study. Foremost among the publishing sources as revealed by the study is the “Computers & Education.” This result provides an important guide to scholars regarding the publishing outlet that is suitable for their research papers.

Additionally, an investigation into relevant articles published in the field revealed that the work of Kinshuk et al. ( 2016 ) stands out; these authors work mainly focused on the transformation of technology-enhanced learning into smart learning environments. Perhaps, their work sets the stage for discussions on the features and characteristics of smart learning environments from the technology and pedagogy perspectives. Similarly, our result delineates that the United States has the highest number of scientific productions in the field of smart learning environments over the years. That suggests that the United States remained the most relevant country in the field of smart learning environments. Regarding institutions’ contributions and relevance, Beijing Normal University in China tops the list. In the aspect of prolific scholars making an immense contribution to the field of smart learning environments, Arthur C. Graesser from the United States tops the list with an h-index of 8. Besides, scholars such as Kinshuk, Graesser, Ogata, De Jong, and Aguilar have established a wide range of collaboration networks.

Furthermore, the study revealed that the field of smart learning environments is recently evolving with the emerging and growing aspects such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning”. The thematic analysis results show that themes such as “digital storytelling” are emerging and connected to smart learning environments. However, this theme and its associated components, such as virtual reality, critical thinking, and serious games, needs to be further developed to establish more ties with “smart learning”. The study further showed that in the mid-year of 2020, “deep learning” remains the trending topic. It is interesting to discover that between 2017 and 2020, newer topics connected to artificial intelligence (AI) such as learning analytics, blockchain, and deep learning, have emerged and grown to become research hotspots in smart learning environments. These findings underscore the importance of deepening further studies to leverage AI in future designs of smart learning environments. As part of our conclusion, some suggestions for future research in the field of smart learning environments are highlighted in this study.

It could be essential to develop more extensive research collaborations between scholars and institutions, thereby creating a more global impact on smart learning environments’ potentials for an enhanced learning experience.

It is suggested that scholars invest more effort in learning analytics, machine learning, and deep learning, as the study shows that they are future research topics in smart learning environments.

More effort into researching digital storytelling, serious games, virtual reality, and critical thinking by educational technologists and designers of smart learning environments is suggested. This study has shown that there are potentials to adopt these strategies in developing twenty-first-century learning.

Study limitations

This study has some limitations. Majorly, the study weakness is about the sample data collection. The study encountered a technical limitation in terms of the software used to conduct the analysis, where the merging of data from different databases was not possible at the time the study was conducted. The sample in this study was collected from the Scopus database, which may result in missing out relevant data. Collecting sample data from multiple independent databases would certainly improve the study in a significant way. In addition, the search keywords used in querying the database could be improved to consist more relevant keywords. This limitation should motivate future work where scholars could explore ways of collecting data from multiple databases with expanded keywords for a more in-depth analysis.

In sum, we conclude that this study hopes its findings will provide insight to researchers, specifically, the young scholars in smart learning environments regarding the research landscape and future research hotspots. For example, young researchers who are beginning to research in the field can quickly identify top articles, prolific authors, and research hotspots in the field of smart learning environments. In addition, the study shows emerging topics in the field of smart learning environments, which needs to be further developed to connect to the objective of smart learning. Findings from this study provide a quick overview of the output in this field over the years and relevant pointer to the future direction in the field of smart learning environments.

Availability of data and materials

The datasets generated during this study are available from the corresponding author.

https://bibliometrix.org/

Abbreviations

Smart Learning Environments

Research Questions

Published Year

Total Citation

Keywords Co-occurrence Network

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Smart University Taxonomy: Features, Components, Systems

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Smart education creates unique and unprecedented opportunities for academic and training organizations in terms of higher standards and innovative approaches to (1) learning and teaching strategies—smart pedagogy, (2) unique highly technological services to local on-campus and remote/online students, (3) set-ups of innovative smart classrooms with easy local/remote student-to-faculty interaction and local/remote student-to-student collaboration, (4) design and development of Web-based rich multimedia learning content with interactive presentations, video lectures, Web-based interactive quizzes and tests, and instant knowledge assessment. This paper presents the outcomes of an ongoing research project aimed to create smart university taxonomy and identify main features, components, technologies and systems of smart universities that go well beyond those in a traditional university with predominantly face-to-face classes and learning activities.

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Acknowledgments

The authors would like to thank Ms. Colleen Heinemann, Mr. Rajat Palod, Mr. Srinivas Karri, Ms. Supraja Talasila, Mr. Siva Margapuri, Ms. Aishwarya Doddapaneni, Mr. Harsh Mehta, Mr. Priynk Bondili, Ms. Divya Doddi, and Ms. Rekha Kondamudi—the research associates of the InterLabs Research Institute and/or graduate students of the Department of Computer Science and Information Systems at Bradley University—for their valuable contributions into this research project.

This research is partially supported by grant REC # 1326809 at Bradley University [ 14 ].

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Uskov, V.L., Bakken, J.P., Pandey, A., Singh, U., Yalamanchili, M., Penumatsa, A. (2016). Smart University Taxonomy: Features, Components, Systems. In: Uskov, V., Howlett, R., Jain, L. (eds) Smart Education and e-Learning 2016. Smart Innovation, Systems and Technologies, vol 59. Springer, Cham. https://doi.org/10.1007/978-3-319-39690-3_1

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A determination of the smartness level of university campuses: the Smart Availability Scale (SAS)

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Technological developments on university campuses are among the most recently investigated topics, but the whole notion of a smart campus has yet to be developed. A smart campus can only be comprehended as a whole, which is why it requires an extensive planning process. This article investigates the required smart campus services with a holistic approach. The smart campus concept has been defined by three major categories: smart building, the scope, and the technology, and then the aspects that affect these categories are defined. A fundamental calculation has been constructed based on the smart campus concept created with newly consolidated categories and a case study with post-occupancy evaluations. The Smart Availability Scale (SAS) calculation is based on superimposing two matrices: campus system output and weighted value matrix. For this calculation, the multi-criteria decision-making (MCDM) method was adopted using newly created index parameters and categories. The technologies selected for this research are based on the most recent developments. It extracted valuable conclusions and inferences from this smart campus conceptual framework, providing insights and directions toward the required calculation technique for the services offered by the smart campus. During the evaluation period of traditional to smart universities, this research draws an outline and guidance for the stakeholders of the affiliated campus.

Introduction

This article’s objective is to clarify the idea of a complex, multifaceted smart university campus and examine what it entails in terms of a smart building, the scope, and the technology, and finally provide a fundamental calculation technique. Instead of a small team of authors, many authors can offer more in-depth knowledge of current research and development in their professions and areas of specialization. Many universities have started smart campus-related projects in specific areas like smart classrooms or smart transportation, but the concept of a smart campus as a whole has yet to be developed [ 33 ]. Therefore, this study presents a holistic approach and explores how to integrate these various fields and disciplines under one umbrella.

Technological advancements, and ultimately the Internet of Things (IoT), have enabled the development of a wide range of smart building solutions. Buildings’ intricate structures and systems, as well as rapid developments in technology and construction, have prompted extensive research over the years. Some research has concentrated on assessing building services [ 30 , 54 , 59 ], but others have focused on other variables such as measuring adaptability or individual comfort factors [ 58 ]. LEED (Leadership in Energy and Environmental Design), BREEAM (Building Research Establishment's Environmental Assessment Method), DGNB (German Sustainable Building Council), and SRI (Smart Readiness Indicator) are just a few of the rating systems and schemes available. As can be seen in Table 1 , the most common system’s targeted typology does not include campuses and instead focuses on different building typologies. Thus, one of the main deficiencies is that the building does not contain technologies that serve its purpose. Furthermore, because these rating systems and schemes exclusively evaluate buildings, outdoor spaces on campuses are not addressed. While there is ongoing research on what constitutes a ‘smart campus’, it is useful to define and combine new parameters that indicate the smartness level of campuses and finally to explain the fundamental calculation technique.

Finding effective project solutions involves more than just using the appropriate methodology in each subject; it also involves using a coordinated execution strategy. Planning strategies for smart campuses should always be integrated to prevent prioritizing one issue over another [ 13 ]. This is crucial in large-scale, intricate operations like the development of university campuses. As a result, information on what is now accessible in terms of smart campus development and a variety of themes will be presented with a motivational all-encompassing strategy to address numerous complicated challenges concurrently. Briefly, this study is devoted to determining the response to the following questions:

RQ1. What are the smart campus parameters (services) to predict the prospective smartness of university campuses?

RQ2. How can a method be developed?

Therefore, to determine the potential smartness of university campuses, a method—the Smart Availability Scale (SAS)—has been developed using newly created parameters (smart building, scope, and technology) for the multi-criteria decision technique.

This study is structured as follows. In the literature review section, smart campuses and services are investigated, and related works are explained. The research method is then described in the fourth section. The study’s fifth section is dedicated to the explanation of the conceptual model of a smart campus. The sixth section is dedicated to the calculation of the SAS score, determining the weighted values, and describing the parameters and value drivers. Then, in the discussion, the Smart Availability Scale is explained. Finally, the research is concluded with implications of findings, conclusions, and limitations of the study.

Literature review

Thousands of students, faculty members, and guests congregate on a university campus, which resembles a small city, to take advantage of the facilities offered by the institution. In other words, the smart campus is compared to a small, autonomous city in terms of its number of features, users, activities, and connections [ 40 ].

Greater sensors, cloud computing, and the Internet of Things (IoT) are the three main advancements that gave rise to the concept of a smart campus recently [ 31 , 34 , 37 , 57 ]. Institutions and academia have developed and presented several ideas regarding the smart campus.

Based on high performance and cloud computing over the Internet, Nie [ 37 ] offered an isolated system for smart campuses with teaching management, school management, a financial system, a library system, and an office system. These systems are the fundamental ones that are accepted for smart campuses. Aion et al. define a concept called iCampus which is made up of six crucial features: iLearning, iGovernance, iGreen, iHealth, iSocial, and iManagement. These features are designed to enhance the student's experience during the teaching-learning process. The iLearning module focuses on the student's capacity for comprehending and accomplishments, with lecture delivery or evaluation, course material, access to materials and books, and other campus resources. The integrated technology of classroom and workspace management is demonstrated in iManagement. A method and process known as iGovernance facilitates institutional responsibility and improves the institution's reputation through actions such as public relations, policies, and procedures. Smart social networking tools including forums, blogs, webpages, Facebook, and Twitter are all incorporated into iSocial. In order to save energy, reduce waste, and protect the environment, iGreen covers the effective use of natural resources. Last, iHealth is a strategy for institutions in clinical healthcare services and student monitoring using wireless technology [ 2 ].

Aiming to provide greater quality services, the smart campus attempts to explain several services. These services broaden their scope by highlighting the campus’ social, financial, and environmental facets in addition to the academic goal. The key is to create a cost-effective system, that effectively uses resources, and offers the campus high-quality services. Numerous advantages of a smart campus include “provide an interactive and creative environment for students and faculty, promote smart energy management, bring effective surveillance system and real-time incident warnings, automate maintenance and business processes, maintain efficient parking and access control management, and provide secure payments and transparent voting systems” [ 4 ]. The core concept of the smart campus is to combine a variety of advanced technologies in order to have high educational performance, provide users with comfort, and be environmentally friendly, according to the various definitions and features. It can be characterized as productive, versatile, and user-centered information technology services that integrate architectural systems and instructional technologies on university campuses and support automation and real-time reporting. A thorough investigation into smart campuses conducted by Muhamad et al. concluded that the fundamental concept of a smart campus is an endeavor to integrate a collection of advanced technologies by the university to enhance performance, the caliber of graduates, and the convenience of life. The availability of information technology services is useful, dynamic, and user-oriented to enable automation and reporting instantaneously, not just for learning activities but covering a wider aspect, including socialization, environment, and, most importantly, the student experience [ 34 ].

Omotayo et al.’s conceptual framework illustrating the infrastructure elements for smart campus study is very comprehensive and has consequences for selecting services for this research. The model concentrates on the components of the campus development infrastructure. Four major sections are formed from the fifteen cluster themes. Smart building construction or reuse, technology and IT networking, continuous improvement, and intelligent learning and teaching systems comprise the four divisions. The first step in enabling smart buildings is the opportunity to modify or retrofit existing campus buildings with IoT. The way a building manages its water use, IoT capabilities, smart meters, energy use, and sustainability all contribute to its smartness. Second, the development of the smart campus buildings includes a smart network grid, which consists of a microgrid on the campus. The inclusion of cloud computing is implied by the existence of a smart campus network grid. Intelligent campus communications, data processing, interfaces, human-computer interaction, and persuasive computing issues can all be addressed by cloud computing infrastructure. Third, the concept of continuous improvement in smart campus management is related to knowing how to improve systems and learning from data and applications already in use. Finally, the learning management system regulates the way students and other campus users engage with smart technologies, smart buildings, and energy use on campus during the teaching and learning process [ 38 ].

Another point that needs to be highlighted while discussing the concept of a smart campus is the definition of smart service, which has become a subject that has been addressed lately, thanks to the Internet of Things (IoT) and big data. A smart service can be regarded as an enhancement of the standard services that are prevalent on today's Internet. The conscious goal of a smart service is to automate and technologically aid daily human operations [ 7 , 10 ]. A smart service seeks to remove human involvement by connecting other services and information fragments. This approach, in particular, offers smart service characteristics such as working with and integrating different data sources, personalized state-based service setup and customization, and proactive service delivery [ 28 ].

The six domains Muhamad et al. classify the smart services as “(1) technologies and systems for intelligent learning, (2) governance, (3) social networks, (4) campus management, (5) health, and (6) green aspects” [ 34 ], specifically for university campuses. The organization of the university's objectives as an academic institution serves as the foundation for this conceptual classification setting [ 26 ]. First and foremost, it seeks to improve teaching and learning. It also offers cost-efficient options for maintaining strong governance and management processes. Then, it is to create balanced and conscientious environmentally friendly solutions for the local and larger scale. Finally, it emphasizes the users’ health. Anagnostopoulos et al.’s five research dimensions are outlined in a proposed taxonomy for IoT-enabled Smart Campus: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology [ 5 ]. Briefly stated, this idea is shaped by incorporating research methodology in addition to hardware and software technologies.

The determination of smart service detection can be resolved by defining unique properties [ 27 ]. According to Rijsdijk and Hultink’s concept of ‘smartness’, this entity has seven dimensions: “autonomy, adaptability, reactivity, multifunctionality, ability to cooperate, humanlike interaction, and personality” [ 41 ]. Akhrif et al. contend that smart services have a variety of characteristics, including “being user-centric, ubiquitous, highly integrated, adaptive, context awareness, and open” [ 3 ]. Briefly, due to the numerous sensors, devices, and physical connectivity, smart service is essentially the merger of management and information systems to provide comfort, health, and well-being to users along with positive energy-related results. This study acknowledges that smartness as a notion can be acquired over time, and all universities are typically equipped with objects and services that represent the potential for gaining smartness. University campuses can be sufficiently equipped with innovative technology on several levels, from the design components to the more intricate architectural spaces.

Related work

Numerous research has provided definitions of the idea and systems of smart campuses from various angles [ 2 , 4 , 14 , 24 , 35 , 37 , 49 , 52 ]. The range of services offered by smart campuses is fairly broad, and some services related to this subject can be listed as follows: heating, cooling, and ventilation; lighting; water; waste; occupant detection, wayfinding and mobility; safety and security; dynamic building components; renewable energy; and educational technology. A conventional university campus can be open to advancement in every one of these areas.

In the European Union (EU), the residential and commercial building sector accounts for about 40% of overall energy consumption [ 16 ]. A large increase in greenhouse gas (GHG) emissions from 19% in 2010 to 39% as a result of this high share of energy use [ 17 ]. Concerns about rising energy needs, their detrimental consequences on the environment, and climate change are prevalent today [ 19 ]. Multiple recent studies have revealed that the HVAC system consumes approximately half of the energy used in buildings [ 29 ]. The new HVAC technology responds well to a variety of factors, including weather, time, occupancy rate, comfort scale, and energy [ 19 , 45 ]. As a result, the value of these factors can be maintained at an optimal level without requiring direct control of the HVAC systems. Another advancement has been observed in lighting systems, which can improve academic productivity by offering comfort to campus occupants [ 47 ]. Lighting systems have evolved in response to changes in building type, space, season, time of day, and occupancy [ 46 ].

Smart water systems improve efficiency, longevity, and reliability through real-time monitoring and automation. These systems are designed to provide effective and efficient power distribution, wastewater distribution, treatment and recovery, water flow, quality, and saturation, and energy conservation [ 39 , 44 ]. Production and consumption are rising as a result of the rise of densely populated cities. Systems for waste collection and separation have arisen in response to the rise in consumption. Solutions were provided by three key advancements in waste technology: smart solid waste management, hardware (sensors), and software [ 18 ].

Wayfinding in university buildings or on campus might be challenging for campus occupants. Due to advanced sensor systems, obtaining support with location technologies is now more advanced. Numerous proximity and navigation technologies, such as optical codes, smart cards, Radio Frequency Identification (RFID), and Near Field Communication (NFC), are being developed [ 11 , 15 ]. These sensor technologies have an impact on practically every system on campus, but one of the most essential applications is safe and secure campuses with access control [ 9 ]. The incorporation of sensors and actuators for effective smart campus surveillance is made possible by IoT technology. Students are discreetly watched in this setting to protect their privacy and human rights [ 5 ]. Briefly, these technologies made it possible to track campus visitors and safely and efficiently manage the equipment. Additionally, it addressed the issue of wayfinding and proposed solutions for visitors with disabilities. In addition, advanced technology enables hazardous source monitoring and early fire warning, fire safety equipment management, and on-site situational assessment [ 12 ]. For network security, developments have been observed on the following topics: “access control, virus and antivirus software, application security, network analytics, types of network-related security (endpoint, web, wireless), firewalls, VPN encryption, and more” [ 36 ]. However, as the number of networked devices is constantly increasing, security solutions and regulations to address the risk of data privacy violations on user data also need to increase.

Elevator and plug load management concerns in active building components have an effect on the campus’ overall energy consumption. The modern elevator trend is shaped by increased security, energy efficiency, and effective crowd management [ 23 ]. Although university campuses include a diversity of building typologies, the end-user behavior pattern is consistent. This provides an opportunity to define the academic community's energy consumption pattern [ 21 ].

The major purpose of making traditional university campuses smart is to improve teaching and learning experiences. The adoption of IoT has undoubtedly built a true foundation for a simple and connected educational environment [ 32 ]. With the advent of digital learning, access to education has been reimagined and expanded. High-quality resources are now accessible to a global audience, and peer-to-peer feedback is made possible. In the last thirty years or more, traditional campuses have transitioned from paper-based to digital to smart campuses, depending on the location of the campus and available resources [ 33 ]. The most recent innovations assist educational settings by adjusting learning environments and empowering students to govern and self-evaluate their learning process using a holistic and ubiquitous approach. A university can get closer to being referred to as a smart university when it implements instructional technologies on campuses along with the smart building solutions mentioned above.

The methodology is principally based on a literature review, a case study with post-occupancy evaluations (POEs), and finally the development of calculations. First, a literature study of current information has been done in order to engage in a conceptual framework for smart campuses. POEs were then implemented and this is because it is widely agreed that the technique provides a method of gathering information that is valuable to all stakeholders in the lifecycle of a building, and that particular components of this information benefit different stakeholders in different ways [ 20 ]. In addition, despite the fact that the transition to smart campus development is still occurring, there is little proof that the opinions of the users are taken into consideration by the decision-making processes [ 1 , 8 ]. Thus, POEs allowed the collection of information to indicate the value of various parameters from an end-user perspective [ 43 ]. Therefore, the data collected from the literature review and POEs were merged to develop the Smart Availability Scale (SAS). The SAS is a calculation method that uses the multi-criteria decision technique to determine the potential smartness of university campuses.

Multi-criteria decision-making (MCDM) is a discipline that allows the simultaneous evaluation of various conflicting criteria in the decision-making process. In most cases, sorting or classification is combined with multi-criteria evaluation problems. Hence, many issues are systematically included in the calculation. In multi-criteria design problems, the variables may be unknown or infinite [ 50 , 53 ]. This technique is often encountered in building performance calculations [ 25 , 48 , 56 ]. Furthermore, several energy, sustainability, and performance-related rating systems and schemes, such as LEED, BREEAM, and SRI, use the multi-criteria decision-making technique [ 53 , 55 ].

Smart campus model

The development of a smart campus can be said to have two key components, according to Xiong’s definition of a smart campus: systems integration and diverse data applications [ 57 ]. Considering this, the definition of a smart campus and the subsequent calculation (SAS Score) are greatly influenced by the accurate determination of the systems within the campus. Consequently, when selecting the services of the campuses with a rather broad range of applications, it is advisable to use classification and categorization methods [ 50 , 53 ]. Three main categories have been identified as defining the smart campus concept and these are smart building, scope, and technology. The conceptual framework for the smart campus model is shown in Fig. 1 .

The smart campus model

First, it has been accepted that smart building features are the key to transforming university campuses into smart. Three categories become prominent under the topic of smart building: perceptual factor, comfort factor, and environmental factor (Table 2 ). The Perceptual factor covers those that an occupant feels with his senses. Several architectural elements directly or indirectly affect the senses of the occupants, such as heating, cooling, and air conditioning (HVAC) systems and lighting systems. The comfort factor covers technological solutions that play a role in the potential for a comfortable and easier life for the occupants. This factor includes occupant detection, wayfinding, and mobility technologies that contribute to campus guidance systems and access control. Because of their impact on psychology, safety and security technologies are also recognized as comfort elements. In addition to focusing on energy management and advantageous energy-efficient technology, smart campuses also aim to boost efficiency. Water, waste, dynamic building components, and renewable energy are the technologies that are evaluated under the subject of environmental factors.

Second, the scope category investigates the subjects that will advance the goal of smart campuses, namely teaching and learning. These are, in essence, smart pedagogy, inclusive technology, and educational technology. A smart campus infrastructure's division for learning and teaching systems depends on on-campus information portals with the addition of digital content and e-learning, smart classrooms, learning analytics, smart pedagogy, and more [ 22 ].

The third category is explained as technology. Design and process come first in this category. It describes the design and delivery tools such as Computer-aided design (CAD) Building Information Modeling (BIM), Geographic Information Systems (GIS), and then simulation, visualization, and decision-making tools such as material flow analysis and life-cycle analysis [ 13 ]. IoT is at the foundation of a smart campus, and the development of the smart campus includes a smart network grid on campus [ 33 ]. Cloud computing will be a part of every smart campus network grid that exists. Finally, data mining and auto-analysis aim to achieve more useful results by replacing human intervention in the decision-making process. Although these three basic categories describe the systems of smart campuses, they are found in different aspects that affect the level of smartness of the systems.

As important as the determination of smart campus services, aspects that determine the level of smartness and in which areas the developments will be recorded should be defined. Thus, nine ‘value drivers’ were defined; comfort, satisfaction, information for occupants, safety, health, energy, preventive measures, integration, and adaptability [ 3 , 6 , 27 , 34 , 41 ]. As a result, the theoretical idea of a smart campus has been defined, and SAS calculations utilize this notion.

The SAS score is an indicator that measures the current level of technology-enhanced university campuses. The targeted typology of the SAS score is specifically university campuses because they operate differently than a unique building.

There are reasons to distinguish this calculation from other performance-related rating systems and schemes. First, this calculation covers all areas of the campus, not just buildings. In other words, all systems required for open areas should be taken into account, for example, transportation on campus, wayfinding, and movement, and security technologies in open areas. In addition, the necessary hardware systems must be suitable for the outdoor environment and applied correctly.

Second, the calculation includes educational technology, for which solutions can overlap with many different systems. For example, RFID sensors are frequently used for security purposes in smart buildings [ 26 ]. However, an RFID sensor can also be used for attendance [ 51 ] and should be listed in educational technologies. Another example is that a closed-circuit camera system (CCTV) installed for security purposes [ 42 ] conflicts with camera systems used for course recording [ 51 ]. Since the two serve different purposes, they should be evaluated differently.

Due to the broad scope of the SAS calculation, context-sorting has helped to determine weighted values. Five categories become prominent: perceptual factor, comfort factor, environmental factor, scope factor, and technology factor. Therefore, the first proposal for weighted values is equal-weighted. Each category is assigned a fixed equal weighted value of 20%.

In Table 2 , 16 subcategories (campus services) are listed: heating; cooling and ventilation; lighting; water; waste; occupant detection, wayfinding, and mobility; safety and security; dynamic building components; renewable energy; educational technology; inclusive technology; smart pedagogy; design and process; smart grid; cloud computing; data mining and auto-analyze. ‘Heating’ and ‘cooling and ventilation’ have been studied as two different campus services. This is because the number of index parameters is vast and must possess a higher weighted value. These sixteen campus services have several index parameters. Although these index parameters belong to a diverse number of campus services, there are a relatively similar number of index parameters in each service.

The defined SAS categories and subcategories can affect the campus in various ways. Therefore, 9 significant value drivers have been listed: comfort; satisfaction; information for occupants; safety; health; energy; preventive measures; integration, and adaptability. Each driver possesses seven levels of performance indicators: − 3, − 2, − 1, 0, + 1, + 2, + 3. The reason behind the adaptation of seven categories is to create a measurement scale similar to that of the European Union Energy Labels. Since many technological products use this labeling system, it will easily facilitate the perception of value. Furthermore, the energy classification of products can be used in a straightforward way to evaluate the energy value driver.

Index parameters represent a list of hardware and software from the latest advanced building and educational technologies that have been categorized through context sorting. Each index parameter has been assigned a code number for convenience in the scoring process. Table 2 demonstrates an example of a rubric for campus system output.

The SAS calculation

The SAS score is calculated as follows:

SAS score = X 1 Y 1 + X 2 Y 2 + X 3 Y 3 + X 4 Y 4 + X 5 Y 5 + X 6 Y 6 + X 7 Y 7 + …. + X 15 Y 15 + X 16 Y 16

Xn = score of 16 subcategories (%)

Yn = weighted value of 16 subcategories (%)

Equal weighted value drivers (Y i )

The following scores are suggested by the authors for equal-weighted value drivers. The calculation gives the numerical values of the campus services that occur when 20% are evenly distributed over the five factors. When categories are assigned equal weighted, subcategory weighted values are:

\({\displaystyle \begin{array}{l}\textrm{SAS}\ \textrm{score}={\textrm{X}}_1\textrm{x}\ 6.66\%+{\textrm{X}}_2\textrm{x}\ 6.66\%+{\textrm{X}}_3\textrm{x}\ 6.66\%+{\textrm{X}}_4\textrm{x}\ 5\%+{\textrm{X}}_5\textrm{x}\ 5\%+{\textrm{X}}_6\textrm{x}\ 10\%+{\textrm{X}}_7\textrm{x}\\ {}10\%+{\textrm{X}}_8\textrm{x}\ 5\%+{\textrm{X}}_9\textrm{x}\ 5\%+{\textrm{X}}_{10}\textrm{x}\ 6.66\%+{\textrm{X}}_{11}\textrm{x}\ 6.66\%+{\textrm{X}}_{12}\textrm{x}\ 6.66\%+{\textrm{X}}_{13}\textrm{x}\ 5\%+{\textrm{X}}_{14}\textrm{x}\ 5\%\\ {}+{\textrm{X}}_{15}\textrm{x}\ 5\%+{\textrm{X}}_{16}\textrm{x}\ 5\%\end{array}}\)

Divergent weighted value drivers (Y i )

Divergent weighted values enable us to obtain more accurate results in the calculation.

Calculation of any Y i value:

where Z ij : ( i ) represents the number of rows where the data are located in the ( j ) column.

For instance,

Y 1 = the subcategory of the heating weighted value

Y 1 = Z 11 + Z 12 + Z 13 + Z 14 + Z 15 + Z 16 + Z 17 + Z 18 + Z 19 = 6.66%

In the table, Z 16 and Z 17 can be accepted for any value, such as 60%, and Z 11 , Z 12 , Z 13 , Z 14 , Z 15 , Z 18 , and Z 19 will be 40% of the value Y 1 .

It is required to proceed with the same calculation for all divergent value subcategories (Y i ).

Subcategory scores (X i )

Determining the nine value drivers for each subcategory should be done by taking the average score of 7 levels of performance indicators. The factor to be considered here is converting the data to a percentage before calculating the SAS score.

Since the scores will be divergent, the calculation of any X i value:

For instance;

X 1 = The subcategory of the heating score

X 1 = W 11 + W 12 + W 13 + W 14 + W 15 + W 16 + W 17 + W 18 + W 19

It is required to proceed using the same calculation for sixteen subcategories (X i ).

Boundary conditions

In some unique cases, certain subcategories or index parameters can be excluded from the calculation of the SAS score.

Therefore, the SAS score is calculated as follows:

The number of removed category or categories = q

SAS score = X 1 Y 1 + X 2 Y 2 + X 3 Y 3 + X 4 Y 4 +…+ X n-q Y n-q

The first step was to review a number of studies on smart buildings. Then, as a separate topic, university technologies, which are prevalent in this area, were studied and their integration into campuses was reviewed. The concept of smartness was also investigated specifically from the technology point of view. One of the most important and comprehensive research studies on the concept of a smart campus was done by Omotayo et al. [ 38 ]. The classification used in this study—systems, subsystems, even hardware, and software—is the key point of distinction. Because this research uses an approximation computation method to draw its conclusions, the interaction between technologies on campuses, such as hardware and software, or which system should generate more value, is taken into consideration.

Broad smart building solutions have been proven feasible through technological developments. The complex systems and structures of buildings, along with the rapid advancements in technology and construction methods, have led to a lot of research over the years. Some have focused on criteria including measuring adaptability or personal comfort factors. Some studies have focused on evaluating building services. Campuses are not included in the most prevalent system's or scheme's intended typology, which instead concentrates on various building typologies. As a result, one of these systems’ fundamental flaws is that they lack technology that is appropriate for space. Furthermore, because most rating systems and schemes only evaluate buildings, they neglect outdoor spaces on campuses. Even if research on what constitutes a ‘smart campus’ is still ongoing, it is helpful to define and combine new characteristics that indicate the smartness level of campuses and, in the end, to describe the basic calculation method.

Systems integration and a wide range of data applications are two essential elements in the development of a smart campus. Given this, the precise identification of the systems on the campus has a significant impact on the definition of a smart campus and the consequent calculation (SAS Score). Therefore, a strategy that incorporates the data from several of the publications under examination has been adopted. The smart campus concept has been broken down into three primary categories: smart buildings, scope, and technology. The first step was to review a number of papers on smart buildings. Then, university technologies that are prevalent in this area were studied and their integration into campuses was reviewed. The concept of smartness was also investigated, specifically the technology and integration category which is a point of difference to other building systems. Since this research uses an approximate calculation method to reach its conclusions, it is important to specify which system should be given more weight or how campus technologies, like hardware and software, relate to one another.

The SAS represents a heuristic scale that focuses on finding class intervals. There are two fundamental reasons for this development. First, the SAS application focuses mainly on understanding the level of smartness of university campuses as a single organism rather than identifying their flaws. Therefore, a heuristic scale facilitates accessibility and communication for this issue. Second, some data for the calculation can be based on personal interpretations. This may evoke certain deviations in the score, and, as a result, this may create an error margin. Therefore, minor deviations in the calculations are tolerated. On this heuristic scale, there are six interval classes (Table 3 ).

Before conducting the SAS assessment, a number of requirements must be completed. The following is an overview of these conditions:

The intended audience must consent to this assessment being made.

Time and money concerns need to be balanced.

Current parameters for the index are required.

The setting for an assessment must be appropriate.

Certain conditions should be considered in the data collection and calculation processes as well. The precision of the SAS score can vary depending on the time and level of expertise. In order to produce more structural results, two different assessment techniques are described. Especially, when it is intended to make a comparison between two universities, the same assessment method must be used (Table 4 ).

The first assessment method provides a more rapid result with a higher error margin compared to assessment 2. It is an efficient method if there is a limited amount of time. In assessment 1, the checklist approach might be employed. This assessment can be done by anyone interested in the subject. The data collection period is approximately 1 week. Since SAS is a heuristic scale, it can be utilized in this rating type. Thus, the range of the campus’ level of smartness is determined.

The second strategy necessitates an extended data collection period on campus. It produces a more thorough and accurate result. Since the evaluation takes longer and the accuracy is higher, the SAS score can be specified along with the scale intervals. There is a data collection period of one year. This assessment can be reported on, indicating the outcome of a more methodical examination. The report can include information concerning reliability.

Another crucial factor that affects the accuracy of the SAS score is the boundary issues that experts can define and manage with a more efficient approach. For example, considering climatic factors, the heating category may not be required for campuses in hot regions. Identifying such cases directly affects the accuracy of the SAS score and it should be clarified which categories or subcategories will be removed before starting the data collection process.

Furthermore, the matrix of divergent weighted value drivers (Yi) provides a more accurate value for nine subcategories. For example, heating, cooling and ventilation, lighting, and renewable energy systems significantly affect energy and preventive measures (value drivers). In this case, their weighted value might be higher than other value drivers. Therefore, by superimposing the two matrices, Yi and Xi, the SAS method is performed in the most efficient way.

In summary, the steps below can be followed while completing the assessment.

Before evaluating the campus, it must be determined whether the prerequisite conditions have been met.

The purpose of the SAS evaluation should be determined for the campus.

It should be determined which assessment method will be used.

Data collection should be done in accordance with the assessment type once it has been decided.

Depending on the assessment method, an equal or divergent weighted matrix should be defined and the SAS calculation should be completed.

Reporting and self-assessment should be performed.

Implications of the findings

In order to clarify the methodological contribution of the SAS, a few factors must be highlighted. Over the years, many schemes examining the level of quality, performance, and sustainability of buildings have been offered in academia and industry. Two factors distinguish this approach from the other calculation methods. To begin, this strategy applies to the entire university campus, not just buildings. In this regard, It differs from others, in that it includes the campus’ outdoor areas. This has an impact on various issues, including wayfinding and movement, campus transportation (within and outside of campus), some security systems, hardware suitability in an outdoor setting, and sensor installations.

The second key difficulty is that this method concentrates primarily on universities, which provide an important purpose, education. As a result, all advancements aimed at improving the teaching and learning experience are included. This is accomplished by incorporating educational technologies within the calculation. These technologies have an impact on the building, either directly or indirectly, and are seen as campus components.

More than just employing the proper technique in each area is required to find effective project solutions; a coordinated execution plan is also necessary. It is important to always integrate planning strategies for smart campuses to avoid making one issue more important than another. This is essential for complex, large-scale projects like building university campuses. In order to solve several complex difficulties concurrently, information on what is now available in terms of smart campus development and a variety of subjects is presented. Briefly, this study offers guidance for researchers who will investigate this topic and those in charge of setting policies for smart campuses.

Conclusions

This study originally proposed a conceptual model based on definitions of smart campus and services and it examines five separate criteria to analyze sixteen campus services. Aspects that define the level of smartness and the areas in which developments will be documented are equally crucial to the decision on smart campus services. Consequently, value drivers for smart campus services are also identified. Using this model, a method, the Smart Availability Scale (SAS), is explained for determining the level of smartness of university campuses. The SAS uses the multi-criteria decision-making (MCDM) technique by superimposing the values of these technologies on campus (campus system output) and a matrix system from equal or divergent weighted values based on the significance of these values (weighted value matrix). The technologies chosen for this study are those that have recently been developed. This study contributes to the current state of knowledge in both theoretical and methodological terms. As a theoretical contribution, campus services are reorganized/redefined to present a conceptual model. Methodologically, its contribution focuses on the correlation between the values of various services using a straightforward computational method. Therefore, this research provides an outline and direction for the affiliated campus stakeholders during the transition from a traditional to a smart university.

Limitations and future works

The limitations of this study were determined by fixing more precise weight values, and the implication of SAS in a case study. The following are potential areas for future research:

Contextual flexibility (building-specific features, season, location)

The application of the calculation

Development of policy objectives

Determination of fixed weight values

The relative frequency and significance of HVAC or hot water energy consumption can be affected by variations in climate, as indicated by different climatic zones. Climate also affects the value of the dynamic building envelope in terms of solar shading and the amount of energy generated by solar panels or wind turbines per unit m 2 . Therefore, contextual flexibility directly affects the correct calculation of the SAS score. Making a choice about a service that is not included in a framework but can be significant is essential. The criteria to be applied to determine relevance in terms of public policy are not always apparent and go beyond simple technical judgments. As a result, if an entire building is missing a space, all of its smart services will be ignored, and the SAS will be renormalized following their exclusion.

One of the aims of this research is to develop a benchmark standard for smart campuses. A standard can be established without leaving much room for individual interpretation. As a result, research on weighted values is open to further development.

Availability of data and materials

1. All data analyzed before this study are included in this published article:

Samancioglu, N., Nuere, S., & Suz, A.A. (2021). Revitalizing a Traditional Campus. Interna- tional Journal of Smart Education and Urban Society, 12(4), 12–26. https://doi.org/10.4018/ijseus.2021100102

2. The dataset (listed index parameters) generated during the current study is available from the corresponding author upon reasonable request.

Abbreviations

Building Information Modeling

Building Intelligence Quotient

Building Research Establishment's Environmental Assessment Method

Computer-Aided Design

Closed-Circuit Television Systems

German Sustainable Building Council

Environmental Performance Criteria

Geographic Information Systems

Hong Kong Building Environmental Assessment Method

Heating, Ventilation, and Air Conditioning

Internet of Things

Leadership in Energy and Environmental Design

Multi-Criteria Decision-Making

Post-Occupancy Evaluations

Radio Frequency Identification

Smart Availability Scale

Smart Readiness Indicator

Virtual Private Network

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Nur Samancioglu is a postdoctoral researcher at Tampere University. She has a Ph.D. degree from the Universidad Politécnica de Madrid. Her doctoral work focuses on assessing the technological readiness of buildings to reveal the corresponding solutions. She holds an MSc in human-centered design methodology from Politecnico di Milano and BFA in Interior Architecture and Environmental Design from Bilkent University. She has had experience as a designer/architect in Turkey, Italy, and the UAE.

Silvia Nuere is a professor in the Department of Mechanical, Chemical, and Industrial Design in the Technical School of Engineering in Industrial Design at the Universidad Politécnica de Madrid, Spain. She teaches Artistic Drawing, Basic Design, Graphic Design, and Visual Communication. She received her Bachelor in Fine Arts in 1989 and her Ph.D. in 2002 from the Universidad Complutense de Madrid, Spain. Her research interests include teaching and learning methods based on Project Oriented Learning and the need of mixing different fields of knowledge such as art, design, and engineering. She has promoted this approach to education through more than 30 Innovation Education Projects and since 2011 she is the Creator and Director of the scientific journal ArDIn. Arte, Diseño e Ingeniería [ArDIn. Art, Design, and Engineering]. She is an author and co-author of more than 50 publications about artistic learning methods and humanistic approaches to education. She has also, as an artist, taken part in more than 20 collective exhibitions and made several illustrations for the Scientific Magazine "Investigación y Ciencia", the Spanish edition of the Scientific American Magazine.

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Samancioglu, N., Nuere, S. A determination of the smartness level of university campuses: the Smart Availability Scale (SAS). J. Eng. Appl. Sci. 70 , 10 (2023). https://doi.org/10.1186/s44147-023-00179-8

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Smart literature review: a practical topic modelling approach to exploratory literature review

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Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.

Introduction

Manual exploratory literature reviews are soon to be outdated. It is a time-consuming process, with limited processing power, resulting in a low number of papers analysed. Researchers, especially junior researchers, often need to find, organise, and understand new and unchartered research areas. As a literature review in the early stages often involves a large number of papers, the options for a researcher is either to limit the amount of papers to review a priori or review the papers by other methods. So far, the handling of large collections of papers has been structured into topics or categories by the use of coding sheets [ 2 , 12 , 22 ], dictionary or supervised learning methods [ 30 ]. The use of coding sheets has especially been used in social science, where trained humans have created impressive data collections, such as the Policy Agendas Project and the Congressional Bills Project in American politics [ 30 ]. These methods, however, have a high upfront cost of time, requiring a prior understanding where papers are grouped by categories based on pre-existing knowledge. In an exploratory phase where a general overview of research directions is needed, many researchers may be dismayed by having to spend a lot of time before seeing any results, potentially wasting efforts that could have been better spent elsewhere. With the advancement of machine learning methods, many of the issues can be dealt with at a low cost of time for the researcher. Some authors argue that when human processing such as coding practice is substituted by computer processing, reliability is increased and cost of time is reduced [ 12 , 23 , 30 ]. Supervised learning and unsupervised learning, are two methods for automatically processing papers [ 30 ]. Supervised learning relies on manually coding a training set of papers before performing an analysis, which entails a high cost of time before a result is achieved. Unsupervised learning methods, such as topic modelling, do not require the researcher to create coding sheets before an analysis, which presents a low cost of time approach for an exploratory review with a large collection of papers. Even though, topic modelling has been used to group large amounts of documents, few applications of topic modelling have been used on research papers, and a researcher is required to have programming skills and statistical knowledge to successfully conduct an exploratory literature review using topic modelling.

This paper presents a framework where topic modelling, a branch of the unsupervised methods, is used to conduct an exploratory literature review and how that can be used for a full literature review. The intention of the paper is to enable the use of topic modelling for researchers by providing a practical approach to topic modelling, where a framework is presented and used on a case step-by-step. The paper is organised as follows. The following section will review the literature in topic modelling and its use in exploratory literature reviews. The framework is presented in “ Method ” section, and the case is presented in “ Framework ” section. “ Discussion ” and “ Conclusion ” sections conclude the paper with a discussion and conclusion.

Topic modelling for exploratory literature review

While there are many ways of conducting an exploratory review, most methods require a high upfront cost of time and having pre-existent knowledge of the domain. Quinn et al. [ 30 ] investigated the costs of different text categorisation methods, a summary of which is presented in Table  1 , where the assumptions and cost of the methods are compared.

What is striking is that all of the methods, except manually reading papers and topic modelling, require pre-existing knowledge of the categories of the papers and have a high pre-analysis cost. Manually reading a large amount of papers will have a high cost of time for the researcher, whereas topic modelling can be automated, substituting the use of the researcher’s time with the use of computer time. This indicates a potentially good fit for the use of topic modelling for exploratory literature reviews.

The use of topic modelling is not new. However, there are remarkably few papers utilising the method for categorising research papers. It has been predominantly been used in the social sciences to identify concepts and subjects within a corpus of documents. An overview of applications of topic modelling is presented in Table  2 , where the type of data, topic modelling method, the use case and size of data are presented.

The papers in Table  2 analyse web content, newspaper articles, books, speeches, and, in one instance, videos, but none of the papers have applied a topic modelling method on a corpus of research papers. However, [ 27 ] address the use of LDA for researchers and argue that there are four parameters a researcher needs to deal with, namely pre-processing of text, selection of model parameters and number of topics to be generated, evaluation of reliability, and evaluation of validity. The uses of topic modelling are to identify themes or topics within a corpus of many documents, or to develop or test topic modelling methods. The motivation for most of the papers is that the use of topic modelling enables the possibility to do an analysis on a large amount of documents, as they would otherwise have not been able to due to the cost of time [ 30 ]. Most of the papers argue that LDA is a state-of-the-art and preferred method for topic modelling, which is why almost all of the papers have chosen the LDA method. The use of topic modelling does not provide a full meaning of the text but provides a good overview of the themes, which could not have been obtained otherwise [ 21 ]. DiMaggio et al. [ 12 ] find a key distinction in the use of topic modelling is that its use is more of utility than accuracy, where the model should simplify the data in an interpretable and valid way to be used for further analysis They note that a subject-matter expert is required to interpret the outcome and that the analysis is formed by the data.

The use of topic modelling presents an opportunity for researchers to add a tool to their tool box for an exploratory and literature review process. Topic modelling has mostly been used on online content and requires a high degree of statistical and technical skill, skills not all researchers possess. To enable more researchers to apply topic modelling for their exploratory literature reviews, a framework will be proposed to lower the requirements for technical and statistical skills of the researcher.

Topic modelling has proven itself as a tool for exploratory analysis of a large number of papers [ 14 , 24 ]. However, it has rarely been applied in the context of an exploratory literature review. The selected topic modelling method, for the framework, is Latent Dirichlet Allocation (LDA), as it is the most used [ 6 , 12 , 17 , 20 , 32 ], state-of-the-art method [ 25 ] and simplest method [ 8 ]. While other topic modelling methods could be considered, the aim of this paper is to enable the use of topic modelling for researchers. For enabling topic modelling for researchers, ease of use and applicability are highly rated, where LDA is easily implemented and understood. Other topic modelling methods could potentially be used in the framework, where reviews of other topic models is presented in [ 1 , 26 ].

The topic modelling method LDA is an unsupervised, probabilistic modelling method which extracts topics from a collection of papers. A topic is defined as a distribution over a fixed vocabulary. LDA analyses the words in each paper and calculates the joint probability distribution between the observed (words in the paper) and the unobserved (the hidden structure of topics). The method uses a ‘Bag of Words’ approach where the semantics and meaning of sentences are not evaluated. Rather, the method evaluates the frequency of words. It is therefore assumed that the most frequent words within a topic will present an aboutness of the topic. As an example, if one of the topics in a paper is LEAN, then it can be assumed that the words LEAN, JIT and Kanban are more frequent, compared to other non-LEAN papers. The result is a number of topics with the most prevalent topics grouped together. A probability for each paper is calculated for each topic, creating a matrix with the size of number of topics multiplied with the number of papers. A detailed description of LDA is found in [ 6 ].

The framework is designed as a step-by-step procedure, where its use is presented in a form of a case where the code used for the analysis is shared, enabling other researchers to easily replicate the framework for their own literature review. The code is based on the open source statistical language R, but any language with the LDA method is suitable for use. The framework can be made fully automated, presenting a low cost of time approach for exploratory literature reviews. An inspiration for the automation of the framework can be found in [ 10 ], who created an online-service, towards processing Business Process Management documents where text-mining approaches such as topic modelling are automated. They find that topic modelling can be automated and argue that the use of a good tool for topic modelling can easily present good results, but the method relies on the ability of people to find the right data, guide the analytical journey and interpret the results.

The aim of the paper is to create a generic framework which can be applied in any context of an exploratory literature review and potentially be used for a full literature review. The method provided in this paper is a framework which is based upon well-known procedures for how to clean and process data, in such a way that the contribution from the framework is not in presenting new ways to process data but in how known methods are combined and used. The framework will be validated by the use of a case in the form of a literature review. The outcome of the method is a list of topics where papers are grouped. If the grouping of papers makes sense and is logical, which can be evaluated by an expert within the research field, then the framework is deemed valid. Compared to other methods, such as supervised learning, the method of measuring validity does not produce an exact degree of validity. However, invalid results will likely be easily identifiable by an expert within the field. As stated by [ 12 ], the use of topic modelling is more for utility than for accuracy.

The developed framework is illustrated in Fig.  1 , and the R-code and case output files are located at https://github.com/clausba/Smart-Literature-Review . The smart literature review process consists of the three steps: pre-processing, topic modelling, and post-processing.

Process overview of the smart literature review framework

The pre-processing steps are getting the data and model ready to run, where the topic-modelling step is executing the LDA method. The post-processing steps are translating the outcome of the LDA model to an exploratory review and using that to identify papers to be used for a literature review. It is assumed that the papers for review are downloaded and available, as a library with the pdf files.

Pre-processing

The pre-processing steps consist of loading and preparing the papers for processing, an essential step for a good analytical result. The first step is to load the papers into the R environment. The next step is to clean the papers by removing or altering non-value-adding words. All words are converted to lower case, and punctuation and whitespaces are removed. Special characters, URLs, and emails are removed, as they often do not contribute to identification of topics. Stop words, misread words and other non-semantic contributing words are removed. Examples of stop words are “can”, “use”, and “make”. These words add no value to the aboutness of a topic. The loading of papers into R can in some instances cause words to be misread, which must either be rectified or removed. Further, some websites add a first page with general information, and these contain words that must be removed. This prevents unwanted correlation between papers downloaded from the same source. Words are stemmed to their root form for easier comparison. Lastly, many words only occur in a single paper, and these should be removed to make computations easier, as less frequent words will likely provide little benefit in grouping papers into topics.

The cleansing process is often an iterative process, as it can be difficult to identify all misread and non-value adding-words a priori. Different papers’ corpora contain different words, which means that an identical cleaning process cannot be guaranteed if a new exploratory review is conducted. As an example, different non-value-adding words exist for the medical field compared to sociology or supply chain management (SCM). The cleaning process is finished once the loaded papers mainly contain value-adding words. There is no known way to scientifically evaluate when the cleaning process is finished, which in some instances makes the cleaning process more of an art than science. However, if a researcher is technically inclined methods, provided in the preText R-package can aid in making a better cleaning process [ 11 ].

LDA is an unsupervised method, which means we do not, prior to the model being executed, know the relationship between the papers. A key aspect of LDA is to group papers into a fixed number of topics, which must be given as a parameter when executing LDA. A key process is therefore to estimate the optimal number of topics. To estimate the number of topics, a cross-validation method is used to calculate the perplexity, as used in information theory, and it is a metric used to evaluate language models, where a low score indicates a better generalisation model, as done by [ 7 , 31 , 32 ]. Lowering the perplexity score is identical to maximising the overall probability of papers being in a topic. Next, test and training datasets are created: the LDA algorithm is run on the training set, and the test set is used to validate the results. The criteria for selecting the right number of topics is to find the balance between a useable number of topics and, at the same time, to keep the perplexity as low as possible. The right number of topics can differ greatly, depending on the aim of the analysis. As a rule of thumb, a low number of topics is used for a general overview and a higher number of topics is used for a more detailed view.

The cross-validation step is used to make sure that a result from an analysis is reliable, by running the LDA method several times under different conditions. Most of the parameters set for the cross-validation should have the same value, as in the final topic modelling run. However, due to computational reasons, some parameters can be altered to lower the amount of computation to save time. As with the number of topics, there is no right way to set the parameters, indicating a trial-and-error process. Most of the LDA implementations have default values set, but in this paper’s case the following parameters were changed: burn-in time, number of iterations, seed values, number of folds, and distribution between training and test sets.

• Topic modelling

Once the papers have been cleaned and a decision has been made on the number of topics, the LDA method can be run. The same parameters as used in the cross-validation should be used as a guidance but for more precise results, parameters can be changed such as a higher number of iterations. The number of folds should be removed, as we do not need a test set, as all papers will be used to run the model. The outcome of the model is a list of papers, a list of probabilities for each paper for each topic, and a list of the most frequent words for each topic.

If an update to the analysis is needed, new papers simply have to be loaded and the post-processing and topic modelling steps can be re-run without any alterations to the parameters. Thus, the framework enables an easy path for updating an exploratory review.

Post-processing

The aim of the post-processing steps is to identify and label research topics and topics relevant for use in a literature review. An outcome of the LDA model is a list of topic probabilities for each paper. The list is used to assign a paper to a topic by sorting the list by highest probability for each paper for each topic. By assigning the papers to the topics with the highest probability, all of the topics contain papers that are similar to each other. When all of the papers have been distributed into their selected topics, the topics need to be labelled. The labelling of the topics is found by identifying the main topic of each topic group, as done in [ 17 ]. Naturally, this is a subjective matter, which can provide different labelling of topics depending on the researcher. To lower the risk of wrongly identified topics, a combination of reviewing the most frequent words for each topic and a title review is used. After the topics have been labelled, the exploratory search is finished.

When the exploratory search has finished, the results must be validated. There are three ways to validate the results of an LDA model, namely statistical, semantic, or predictive [ 12 ]. Statistical validation uses statistical methods to test the assumptions of the model. An example is [ 28 ], where a Bayesian approach is used to estimate the fit of papers to topics. Semantic validation is used to compare the results of the LDA method with expert reasoning, where the results must make semantic sense. In other words, does the grouping of papers into a topic make sense, which ideally should be evaluated by an expert. An example is [ 18 ], who utilises hand coding of papers and compare the coding of papers to the outcome of an LDA model. Predictive validation is used if an external incident can be correlated with an event not found in the papers. An example is in politics where external events, such as presidential elections which should have an impact on e.g. press releases or newspaper coverage, can be used to create a predictive model [ 12 , 17 ].

The chosen method for validation in this framework is semantic validation. The reason is that a researcher will often be or have access to an expert who can quickly validate if the grouping of papers into topics makes sense or not. Statistical validation is a good way to validate the results. However, it would require high statistical skills from the researchers, which cannot be assumed. Predictive validation is used in cases where external events can be used to predict the outcome of the model, which is seldom the case in an exploratory literature review.

It should be noted that, in contrast to many other machine learning methods, it is not possible to calculate a specific measure such as the F-measure or RMSE. To be able to calculate such measures, there must exist a correct grouping of papers, which in this instance would often mean comparing the results to manually created coding sheets [ 11 , 19 , 20 , 30 ]. However, it is very rare that coding sheets are available, leaving the semantic validation approach as the preferred validation method. The validation process in the proposed framework is two-fold. Firstly, the title of the individual paper must be reviewed to validate that each paper does indeed belong in its respective topic. As LDA is an unsupervised method, it can be assumed that not all papers will have a perfect fit within each topic, but if the majority of papers are within the theme of the topic, it is evaluated to be a valid result. If the objective of the research is only an exploratory literature review, the validation ends here. However, if a full literature review is conducted, the literature review can be viewed as an extended semantic validation method. By reviewing the papers in detail within the selected topics of research, it can be validated if the vast majority of papers belong together.

Using the results from the exploratory literature review for a full literature review is simple, as all topics from the exploratory literature review will be labelled. To conduct the full literature review, select the relevant topics and conduct the literature review on the selected papers.

To validate the framework, a case will be presented, where the framework is used to conduct a literature review. The literature review is conducted in the intersection of the research fields analytics, SCM, and enterprise information systems [ 3 ]. As the research areas have a rapidly growing interest, it was assumed that the number of papers would be large, and that an exploratory review was needed to identify the research directions within the research fields. The case used broadly defined keywords for searching for papers, ensuring to include as many potentially relevant papers as possible. Six hundred and fifty papers were found, which were heavily reduced by the use of the smart literature review framework to 76 papers, resulting in a successful literature review. The amount of papers is evaluated to be too time-consuming for a manual exploratory review, which provides a good case to test the smart literature review framework. The steps and thoughts behind the use of the framework are presented in this case section.

The first step was to load the 650 papers into the R environment. Next, all words were converted to lowercase and punctuation, whitespaces, email addresses, and URLs were removed. Problematic words were identified, such as words incorrectly read from the papers. Words included in a publisher’s information page were removed, as they add no semantic value to the topic of a paper. English stop words were removed, and all words were stemmed. As a part of an iterative process, several papers were investigated to evaluate the progress of cleaning the papers. The investigations were done by displaying words in a console window and manually evaluating if more cleaning had to be done.

After the cleaning steps, 256,747 unique words remained in the paper corpus. This is a large number of unique words, which for computational reasons is beneficial to reduce. Therefore, all words that did not have a sparsity or likelihood of 99% to be in any paper were removed. The operation lowered the amount of unique words to 14,145, greatly reducing the computational needs. The LDA method will be applied on the basis of the 14,145 unique words for the 650 papers. Several papers were manually reviewed, and it was evaluated that removal of the unique words did not significantly worsen the ability to identify main topics of the paper corpus.

The last step of pre-processing is to identify the optimal number of topics. To approximate the optimal number of topics, two things were considered. The perplexity was calculated for different amounts of topics, and secondly the need for specificity was considered.

At the extremes, choosing one topic would indicate one topic covering all papers, which will provide a very coarse view of the papers. On the other hand, if the number of topics is equal to the number of papers, then a very precise topic description will be achieved, although the topics will lose practical use as the overview of topics will be too complex. Therefore, a low number of topics was preferred as a general overview was required. Identifying what is a low number of topics will differ depending on the corpus of papers, but visualising the perplexity can often provide the necessary aid for the decision.

The perplexity was calculated over five folds, where each fold would identify 75% of the papers for training the model and leave out the remaining 25% for testing purposes. Using multiple folds reduces the variability of the model, ensuring higher reliability and reducing the risk of overfitting. For replicability purposes, specific seed values were set. Lastly, the number of topics to evaluate is selected. In this case, the following amounts of topics were selected: 2, 3, 4, 5, 10, 20, 30, 40, 50, 75, 100, and 200. The perplexity method in the ‘topicmodels’ R library is used, where the specific parameters can be found in the provided code.

The calculations were done over two runs. However, there is no practical reason for not running the calculations in one run. The first run included all values of number of topics below 100, and the second run calculated the perplexity for 100 and 200 number of topics. The runtimes for the calculations were respectively 9 and 10 h on a standard issue laptop. The combined results are presented in Fig.  2 , and the converged results can be found in the shared repository.

5-Fold cross-validation of topic modelling. Results of cross-validation

The goal in this case is to find the lowest number of topics, which at the same time have a low perplexity. In this case, the slope of the fitted line starts to gradually decline at twenty topics, which is why the selected number of topics is twenty.

Case: topic modelling

As the number of topics is chosen, the next step is to run the LDA method on the entire set of papers. The full run of 650 papers for 20 topics took 3.5 h to compute on a standard issue laptop. An outcome of the method is a 650 by 20 matrix of topic probabilities. In this case, the papers with the highest probability for each topic were used to allocate the papers. The allocation of papers to topics was done in Microsoft Excel. An example of how a distribution of probabilities is distributed across topics for a specific paper is depicted in Fig.  3 . Some papers have topic probability values close to each other, which could indicate a paper belonging to an intersection between two or more topics. These cases were not considered, and the topic with the highest probability was selected.

Example of probability distribution for one document (Topic 16 selected)

The allocation of papers to topics resulted in the distribution depicted in Fig.  4 . As can be seen, the number of papers varies for each topic, indicating that some research areas have more publications than others do.

Distribution of papers per topic

Next step is to process the findings and find an adequate description of the topics. A combination of reviewing the most frequent words and a title review was used to identify the topic names. Practically, all of the paper titles and the most frequent words for each topic, were transferred to a separate Excel spreadsheet, providing an easy overview of paper titles. An example for topic 17 can be seen in Table  3 . The most frequent words for the papers in topic 17 are “data”, “big” and “analyt”. Many of the paper titles also indicate usage of big data and analytics for application in a business setting. The topic is named “Big Data Analytics”.

The process was repeated for all other topics. The names of the topics are presented in Tables  4 and 5 .

Based on the names of the topics, three topics were selected based on relevancy for the literature review. Topics 5, 13, and 17 were selected, with a total of 99 papers. In this specific case, it was deemed that there might be papers with a sub-topic that is not relevant for the literature review. Therefore, an abstract review was conducted for the 99 papers, creating 10 sub-topics, which are presented in Table  6 .

The sub-topics RFID, Analytical Methods, Performance Management, and Evaluation and Selection of IT Systems were evaluated to not be relevant for the literature review. Seventy-six papers remained, grouped by sub-topics.

The outcome of the case was an overview of the research areas within the paper corpus, represented by the twenty topics and the ten sub-topics. The selected sub-topics were used to conduct a literature review. The validation of the framework consisted of two parts. The first part addressed the question of whether the grouping of papers, evaluated by the title and keywords, makes sense and the second part addressed whether the literature review revealed any misplaced papers. The framework did successfully place the selected papers into groups of papers that resemble each other. There was only one case where a paper was misplaced, namely that a paper about material informatics was placed among the papers in the sub-topic EIS and Analytics. The grouping and selection of papers in the literature review, based on the framework, did make semantic sense and was successfully used for a literature review. The framework has proven its utility in enabling a faster and more comprehensive exploratory literature review, as compared to competing methods. The framework has increased the speed for analysing a large amount of papers, as well as having increased the reliability in comparison with manual reviews as the same result can be obtained by running the analysis once again. The transparency in the framework is higher than in competing methods, as all steps of the framework are recorded in the code and output files.

This paper presents an approach not often found in academia, by using machine learning to explore papers to identify research directions. Even though the framework has its limitations, the results and ease of use leave a promising future for topic-modelling-based exploratory literature reviews.

The main benefit of the framework is that it provides information about a large number of papers, with little effort on the researcher’s part, before time-costly manual work is to be done. It is possible, by the use of the framework, to quickly navigate many different paper corpora and evaluate where the researchers’ time and focus should be spent. This is especially valuable for a junior researcher or a researcher with little prior knowledge of a research field. If default parameters and cleaning settings can be found for the steps in the framework, a fully automatic grouping of papers could be enabled, where very little work has to be done to achieve an overview of research directions. From a literature review perspective, the benefit of using the framework is that the decision to include or exclude papers for a literature review will be postponed to a later stage where more information is provided, resulting in a more informed decision-making process. The framework enables reproducibility, as all of the steps in the exploratory review process can be reproduced, and enables a higher degree of transparency than competing methods do, as the entire review process can, in detail, be evaluated by other researchers.

There is practically no limit of the number of papers the framework is able to process, which could enable new practices for exploratory literature reviews. An example is to use the framework to track the development of a research field, by running the topic modelling script frequently or when new papers are published. This is especially potent if new papers are automatically downloaded, enabling a fully automatic exploratory literature review. For example, if an exploratory review was conducted once, the review could be updated constantly whenever new publications are made, grouping the publications into the related topics. For this, the topic model has to be trained properly for the selected collection of papers, where it can be assumed that minor additions of papers would likely not warrant any changes to the selected parameters of the model. However, as time passes and more papers are processed, the model will learn more about the collection of papers and provide a more accurate and updated result. Having an automated process could also enable a faster and more reliable method to do post-processing of the results, reducing the post-analysis cost identified for topic modelling by [ 30 ], from moderate to low.

The framework is designed to be easily used by other researchers by designing the framework to require less technical knowledge than a normal topic model usage would entail and by sharing the code used in the case work. The framework is designed as a step-by-step approach, which makes the framework more approachable. However, the framework has yet not been used by other researchers, which would provide valuable lessons for evaluating if the learning curve needs to be lowered even further for researchers to successfully use the framework.

There are, however, considerations that must be addressed when using the smart literature review framework. Finding the optimal number of topics can be quite difficult, and the proposed method of cross-validation based on the perplexity presented a good, but not optimal, solution. An indication of why the number of selected topics is not optimal is the fact that it was not possible to identify a unifying topic label for two of the topics. Namely topics 12 and 20, which were both labelled miscellaneous. The current solution to this issue is to evaluate the relevancy of every single paper of the topics that cannot be labelled. However, in future iterations of the framework, a better identification of the number of topics must be developed. This is a notion also recognised by [ 6 ], who requested that researchers should find a way to label and assign papers to a topic other than identifying the most frequent words. An attempt was made by [ 17 ] to generate automatic labelling on press releases, but it is uncertain if the method will work in other instances. Overall, the grouping of papers in the presented case into topics generally made semantic sense, where a topic label could be found for the majority of topics.

A consideration when using the framework is that not all steps have been clearly defined, and, e.g., the cleaning step is more of an art than science. If a researcher has no or little experience in coding or executing analytical models, suboptimal results could occur. [ 11 , 25 , 27 ] find that especially the pre-processing steps can have a great impact on the validity of results, which further emphasises the importance of selecting model parameters. However, it is found that the default parameters and cleaning steps set in the code provided a sufficiently valid and useable result for an exploratory literature analysis. Running the code will not take much of the researcher’s time, as the execution of code is mainly machine time, and verifying the results takes a limited amount of a researcher time.

Due to the semantic validation method used in the framework, it relies on the availability of a domain expert. The domain expert will not only validate if the grouping of papers into topics makes sense, but it is also their responsibility to label the topics [ 12 ]. If a domain expert is not available, it could lead to wrongly labelled topics and a non-valid result.

A key issue with topic modelling is that a paper can be placed in several related topics, depending on the selected seed value. The seed value will change the starting point of the topic modelling, which could result in another grouping of papers. A paper consists of several sub-topics and depending on how the different sub-topics are evaluated, papers can be allocated to different topics. A way to deal with this issue is to investigate papers with topic probabilities close to each other. Potential wrongly assigned papers can be identified and manually moved if deemed necessary. However, this presents a less automatic way of processing the papers, where future research should aim to improve the assignments of papers to topics or create a method to provide an overview of potentially misplaced papers. It should be noted that even though some papers can be misplaced, the framework provides outcome files than can easily be viewed to identify misplaced papers, by a manual review.

As the smart literature review framework heavily relies on topic modelling, improvements to the selected topic model will likely present better results. The results of the LDA method have provided good results, but more accurate results could be achieved if the semantic meaning of the words would be considered. The framework has only been tested on academic papers, but there is no technical reason to not include other types of documents. An example is to use the framework in a business context to analyse meeting minutes notes to analyse the discussion within the different departments in a company. For this to work, the cleaning parameters would likely have to change, and another evaluation method other than a literature review would be applicable. Further, the applicability of the framework has to be assessed on other streams of literature to be certain of its use for exploratory literature reviews at large.

This paper aimed to create a framework to enable researchers to use topic modelling to, do an exploratory literature review, decreasing the need for manually reading papers and, enabling the possibility to analyse a greater, almost unlimited, amount of papers, faster, more transparently and with greater reliability. The framework is based upon the use of the topic model Latent Dirichlet Allocation, which groups related papers into topic groups. The framework provides greater reliability than competing exploratory review methods provide, as the code can be rerun on the same papers, which will provide identical results. The process is highly transparent, as most decisions made by the researcher can be reviewed by other researchers, unlike, e.g., in the creation of coding sheets. The framework consists of three main phases: Pre-processing, Topic Modelling, and Post-Processing. In the pre-processing stage, papers are loaded, cleaned, and cross-validated, where recommendations to parameter settings are provided in the case work, as well as in the accompanied code. The topic modelling step is where the LDA method is executed, using the parameters identified in the pre-processing step. The post-processing step creates outputs from the topic model and addresses how validity can be ensured and how the exploratory literature review can be used for a full literature review. The framework was successfully used in a case with 650 papers, which was processed quickly, with little time investment from the researcher. Less than 2 days was used to process the 650 papers and group them into twenty research areas, with the use of a standard laptop. The results of the case are used in the literature review by [ 3 ].

The framework is seen to be especially relevant for junior researchers, as they often need an overview of different research fields, with little pre-existing knowledge, where the framework can enable researchers to review more papers, more frequently.

For an improved framework, two main areas need to be addressed. Firstly, the proposed framework needs to be applied by other researchers on other research fields to gain knowledge about the practicality and gain ideas for further development of the framework. Secondly, research in how to automatically identity model parameters could greatly improve the usability for the use of topic modelling for non-technical researchers, as the selection of model parameters has a great impact on the result of the framework.

Availability of data and materials

https://github.com/clausba/Smart-Literature-Review (No data).

Abbreviations

• Latent Dirichlet Allocation

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CBA wrote the paper, developed the framework and executed the case. CM Supervised the research and developed the framework. Both authors read and approved the final manuscript.

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Asmussen, C.B., Møller, C. Smart literature review: a practical topic modelling approach to exploratory literature review. J Big Data 6 , 93 (2019). https://doi.org/10.1186/s40537-019-0255-7

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