essay scoring online

Free Online Paper Grader Calculator: Rate Your Essay In Seconds

Use our ultrapowerful, fully free paper rater to accurately grade your essay before submitting it. Get deep and extensive feedback for perfecting your written assignments.

How to Use Our Free Essay Revisor Online?

Now you can revise essay online free without registration or spending money. Follow these three simple steps.

Do you see this big area in the middle? Type in or copy-paste your text into the box. Check whether your text meets size requirements.

Online essay revision free is done automatically in the background. After evaluation, results and grades will appear on the screen.

Evaluate your mistakes, correct them, and improve your writing skills! Feel free to edit your essay right in the input window.

Get Expert Help

StudyCrumb is a globally trusted company delivering academic writing assistance. Backed by qualified writers, we provide unique academic papers tailored to clients specific needs.

Take your writing to a whole new level with our editing and proofreading services. Our academic proofreaders will fine-tune your essay and make it impeccable.

Why Choose StudyCrumb

Why Choose Our Free Online Essay Grader Tool?

Finding a good free essay grader online is a real pain for each student. Some services provide miserably small feedback. Others are too detailed and overloaded. During the development of our tool, we did our best to eliminate all mistakes of our competitors. Here are four reasons to choose our tool.

Feel free to score essays online without breaking a bank. It even gets better – do all that and much more without spending a single penny.

Beautifully crafted design of our automatic essay grader free online is a true feast for the eyes. A refined and intuitive interface is miles ahead of the competition.

Grading papers online has never been so fast. Blazing speeds with the professional quality of assessment. Enjoy the best of both worlds right after the input.

Big brother is surely watching, but will never know that you grade essay before sending it. No data is stored on servers or sold away.

Features of Free Paper Grader for Students

Just like with pokemons, paper grader online free services have their own unique features. Choosing the right one can significantly increase your writing efficiency and skills. If you want your papers and essays to be amazing, you have to select our writing rater. Here are some features of it to back us up on this:

Grammar grader is one of the core functions of our tool. Without correct spelling and sentence construction, even the smartest text will look boring. Smart algorithms and advanced Artificial Intelligence see tiny little mistakes in words and sentences.

Improve originality of your work by checking it in our essay revisor free of charge! Enormous databases and the latest advancements in machine learning can find even the slightest resemblances between essays. So, pay attention to what you’re copying.

Can our online essay scorer free people from boring texts? Yes, it can. Investigate your essays even further with readability scoring. Try keeping your text on point at all times. Brevity is the soul of wit, as they say.

Complete analysis and assessment are available in the final online essay review. Just glance at it and get precise and in-depth information about your writing skills. With some time and effort, you will definitely get better!

Grade My Essay for Free, StudyCrumb

One of the most popular searches among students is “grade my essay free”. It is not hard to understand scholars. Colleges from all over the world are now loading their pupils with absurd amounts of essays. Tens of research papers per studying year, writing all day long. And with all that pressure students are forced to maintain good grades. Essay topics never change, but they expect original thoughts from students. How is it fair? Let’s imagine a situation. I am a college student. Each day I wake up at 6 am and start writing. Finally, three hours of hard labor finally bore fruit – an essay. I can’t submit it straight away. I have to rate my essay online so I can fix all problems and resolve all issues. Only after I grade my college essay on a trusted website I can send it to my professor and be sure of getting a good grade.

Reasons to Use Our Grade My Paper Calculator 

I can grade my paper free online! Yes! It finally happened! (We hope you don’t mind us continuing our monologue from the perspective of a student.) But why exactly would I use this particular website that grades papers?

• Three-in-one solution I can finally check what grade is my writing going to get, evaluate the readability of my essay, and grade my paper for plagiarism at the same time! It's like readability checker , plagiarism checker and writing checker in one paper grading tool.
• No registration Finally, a decent service that does not require your passport details and the names of your pets to operate. At least somewhere my private life stays private.
• I pay nothing No fees or hidden payments – my money stays in my pocket. With that measly sum, I have each month for expenses, I can buy more food. God bless those altruists for helping students of the world!
• Easy to use My computer can’t handle another app, no space on a hard drive. All that hustle and fuss are long gone. Now I can check my texts everywhere, without downloading anything. One second and I know everything I need to know.

Use Student Essay Scorer Online to Improve Your Writing

Now you have found a perfect grader tool for free essay scoring. After you write something, just insert your final version into the box on our website that grades your essay.  Based on received feedback and smart suggestions, you will be able to fix typing mistakes, spelling errors, increase the readability and quality of your work. The writing was never an easy thing to master, so any help will be greatly appreciated. Especially if that help comes at the right time and provides the right amount of information.  This exact balance makes our tool so great. It does not overpower you with red markers and warning signs. It casually and friendly says “here are some of the mistakes I’ve noticed. Would you like to solve them?” Finally, you can stop looking for other ways to “score my essay”.

Haven't started writing? Delegate your " do my essay " task to StudyCrumb and get supreme academic service. 

Rate My Paper Free: Grade Any Type of Academic Writing

“Help me rate my writing! Please, rate my paper grammar!” That’s how one morning began for us a few years back. An email from a student, depicting the unjust reality of college academic writing. We saw it as an opportunity to help, so the development of a proficient online content checker began. After a number of sleepless hours connecting AI to machine learning, it was done. Finally, a beautiful unicorn. The one and only, friendlier one among paper raters. Now, our software is capable of proofreading, plagiarism and grammar checking, and formatting every type of written document there is. Any level of difficulty, fully automatic rating, available 24/7. Here are some short descriptions of our most popular grading tasks.

Automatic Essay Grading

Free essay review online is completely automatic now! No more need to press those prehistoric buttons, everything happens in the background. It happens so fast, that results will appear on the screen faster than you say “review my essay free please”. Advanced information technologies and algorithms are always ready to serve you.  Essay evaluator online is free, easy to use, and yields fantastic results. It will show your weaknesses, and show smart suggestions on how to improve your writing. Isn’t that what every student wants? Clear and unobtrusive experience. Modern product to satisfy somewhat redundant needs and fit annoying requirements.

There is only one case when your won't need a paper grader. Academic works delivered by our college paper writing service are so great that you won;t need any essay rater.

Online Research Paper Grader

Access this research paper rater free online and get your article professionally assessed in a blink of an eye! No more “where can I grade my paper free” questions – you have the website, you know what to do. Do it! Don’t even try submitting your article without checking it. No commission will allow you to fix your mistakes after the submission. And what if the plagiarism percentage is too high? Trust us, you don’t want all that. Do you want a clean entry with high scores? Then use our free college paper grader to improve your texts right now! In case you haven't written your project, try our research paper services . This way you will get a high-quality paper that eets all requirements. 

Thesis Grader

“I am a happy student now, my favorite thesis rater can now rate my thesis!” Those words we expect to hear from you on short notice. Your thesis is getting closer, and we hope you have started working on it already. If you have not, don’t wait for too long and hire an experienced thesis writer . Time is running out, as always. After you type the last letter, take some time to evaluate your thesis. Check for mistakes, spelling errors, assess plagiarism and readability. Fortunately, you now know just the right place to do it – StudyCrumb! Check it, improve it, and get your A+!

Who Can Use Our Essay Rater to Grade Papers

Who do you think uses our essay tester? Aliens? No! Average people, just like you. There are plenty of people who need their texts checked and corrected. Since it’s hard to find a part of modern life or profession where the writing of some sort is not involved, just about everyone uses it. Parents are using it as a school paper grader to help their kids. Teachers and professors use it as college essay grader. No modern education institution can live without essay or paper rating. However, it is necessary to discuss specifics, get to those details, look in every nook and cranny. Let’s have a glimpse at three main categories of our users.

Online Paper Grader for Students

Grading college papers is a pain for every student out there. But writing those papers is even worse. You have to come up with an idea, turn an idea into words, words into sentences, and so on. And even after you’re done, you have one more step – grading paper. You can ignore it, but how would you know your weaknesses? Please, use our grading papers calculator to check your essays so you could always get the best marks and stay on top!

Free Essay Grading Software for Teachers

Almost every teacher has a lot of essays to check, so essay grader for teachers free must change the game! No need to check them manually, just copy and paste a student's text to our website and get the instant score.  Paper grader for teachers can become the main way of evaluating students. Also, a teacher can specify which service students should use so everyone will be on the same page when it comes to essay or paper quality.

Online Paper Rater for Writers

Writers rarely need to rate essay. Paper graders free are also not their choice. Writers need a powerful instrument that can evaluate on a far more complex level and provide deep insights, and the tool should account for that. However, we managed to tune our tool just about right so writers could use it for their needs without being slapped in the face with the truth. Now, thousands of writers check their texts here and improve them with our help.

Tired of writing your own essays?

Entrust your task to StudyCrumb and get a paper tailored to your needs.

FAQ About Automatic Paper Grader

Some of you probably have some questions left regarding automated essay scoring online. Please, check these answers below:

1. Is your essay grader free?

We are a proud fully free website that grades essays. We strongly believe that every student must have the ability to grade and rate their essays before sending them. Our tools also serve another purpose – improving writing quality among teachers and scholars of universities and colleges.

2. Who can revise my paper for free?

Our paper grader for free will do it! Instead of employing editors and writers, we gave this job to intelligent machines. The quality is better, more tasks can be done simultaneously, and we manage to keep our tool absolutely and utterly free! Looking forward to working with you!

3. Do I need to register to grade my writing?

Fortunately, no registration is needed for online paper grader free. Your personal information stays personal. We don’t care who you are. All we care about is providing the highest quality proofreading and text rating at zero price. Just paste your essay and get instant results!

4. How to make my paper better?

After you get feedback from paper grading software, look at your weak spots. Determine main problems and try fixing them one at a time. To fix grammar, pay more attention to what you are reading online. For fixing plagiarism – rewrite your text or use our rewriter tool. You got the gist?

Other Tools You May Like

StudyCrumb goes beyond just a paper grader tool. Discover our suite of free writing tools designed to enhance your academic experience. Explore them below!

All you have to do is type in or paste your text below this instruction and click Check text to get all the results. Click on the highlighted spelling error or grammar improvements

• Grammar Checker
• Paraphrasing Tool
• Critique Report
• Writing Reports
• Learn Blog Grammar Guide Community Events FAQ
• Grammar Guide

Essay checker: free online paper corrector

Your best chance for an A+ essay. Try our free essay checker below.

Start typing, paste, or use

Get more suggestions to enhance this text and all your future writing

Your suggestions will show once you've entered some text.

Great job! We didn't find any suggestions in your text.

Why should you use a free essay checker?

The simple answer? Good grammar is necessary, but it's not easy. You've already done countless hours of research to write the essay. You don't want to spend countless hours correcting it too.

You'll get a better grade

Good grammar, or its absence, can determine if you get a good grade or a failing one. Impress your lecturer not just with how grammatically sound your writing is but how clear it is and how it flows.

You'll save time

Essay writing can be a long and tedious process. ProWritingAid's essay checker saves you the hassle by acting as the first line of defense against pesky grammar issues.

You'll become a better writer

Essay writing is a particular skill and one that becomes better with practice. Every time you run your essay through ProWritingAid's essay corrector, you get to see what your common mistakes are and how to fix them.

Good Writing = Good Grades

It's already hard to know what to write in an essay. Don't let grammar mistakes hinder your writing and prevent you from getting a good grade. ProWritingAid's essay checker will help you write your best essay yet. Since the checker is powered by AI, using it means that grammar errors don't stand a chance. Give your professors something to look forward to reading with clear, concise, and professional writing.

How does ProWritingAid's essay checker work?

Your goal in essay writing is to convey your message as best as possible. ProWritingAid's essay checker is the first step toward doing this.

Get rid of spelling errors

ProWritingAid's essay checker will show you what it thinks are spelling errors and present you with possible corrections. If a word is flagged and it's actually spelled correctly, you can always choose to ignore the suggestion.

Fix grammar errors

Professors aren't fans of poor grammar because it interrupts your message and makes your essay hard to understand. ProWritingAid will run a grammar check on your paper to ensure that your message is precise and is being communicated the way you intended.

Get rid of punctuation mistakes

A missing period or comma here and there may not seem that serious, but you'll lose marks for punctuation errors. Run ProWritingAid's essay checker to use the correct punctuation marks every time and elevate your writing.

Improve readability

Make sure that in the grand scheme, your language is not too complicated. The essay checker's built-in Readability report will show if your essay is easy or hard to read. It specifically zones in on paragraphs that might be difficult to read so you can review them.

What else can the essay checker do?

The editing tool analyzes your text and highlights a variety of key writing issues, such as overused words, incohesive sentence structures, punctuation issues, repeated phrases, and inconsistencies.

You don't need to drown your essay in words just to meet the word count. ProWritingAid's essay checker will help to make your words more effective. You'll get to construct your arguments and make sure that every word you use builds toward a meaningful conclusion.

Transition words help organize your ideas by showing the relationship between them. The essay checker has a built-in Transition report that highlights and shows the percentage of transitions used in your essay. Use the results to add transitions where necessary.

An engaging essay has sentences of varying lengths. Don't bore your professor with long, rambling sentences. The essay checker will show you where you need to break long sentences into shorter sentences or add more sentence length variation.

Generally, in scholarly writing, with its emphasis on precision and clarity, the active voice is preferred. However, the passive voice is acceptable in some instances. When you run your essay through ProWritingAid's essay checker, you get feedback on whether you're using the passive or active voice to convey your idea.

There are specific academic power verbs, like appraise , investigate , debunk , support , etc., that can add more impact to your argument by giving a more positive and confident tone. The essay checker will check your writing for power verbs and notify you if you have less than three throughout your essay.

It's easy to get attached to certain phrases and use them as crutches in your essays, but this gives the impression of boring and repetitive writing. The essay checker will highlight your repeats and suggest contextually relevant alternatives.

Gain access to in-house blog reports on citations, how to write a thesis statement, how to write a conclusion, and more. Venture into a world of resources specific to your academic needs.

What kinds of papers does ProWritingAid correct?

No matter what you're writing, ProWritingAid will adapt and show you where your edits are needed most.

• Argumentative
• Descriptive
• Textual analysis
• Lab reports
• Case studies
• Literature reviews
• Presentations
• Dissertations
• Research papers

Professors and students love using ProWritingAid

If you're an English teacher, you need to take a look at this tool - it reinforces what you're teaching, highlights strengths and weaknesses, and makes it easier to personalize instruction.

Jennifer Gonzales

Only reason I managed to get an A in all my freshman composition classes.

Chris Layton

Great tool for academic work. Easy to use, and the reports and summary evaluation of your documents in several categories is very useful. So much more than spelling and grammar!

Debra Callender

Questions & Answers

1. how do i use the essay checker online tool.

You can either copy and paste your essay in the essay checker field or upload your essay from your computer. Your suggestions will show once you enter text. You'll see a number of possible grammar and spelling issues. Sign up for free to get unlimited suggestions to improve your writing style, grammar, and sentence structure. Avoid unintentional plagiarism with a premium account.

2. Does the essay checker work with British English and American English?

The essay checker works with both British English and American English. Just choose the one you would like to use and your corrections will reflect this.

3. Is using an essay checker cheating?

No. The essay checker won't ever write the essay for you. It will point out possible edits and advise you on changes you need to make. You have full autonomy and get to decide which changes to accept.

4. Will the essay checker autocorrect my work?

The essay writing power remains in your hands. You choose which suggestions you want to accept, and you can ignore those that you don't think apply.

5. Is there a student discount?

Students who have an eligible student email address can get 20% off ProWritingAid Premium. You can apply for a student discount through Student App Centre .

6. Does ProWritingAid have a plagiarism checker?

Yes. ProWritingAid's plagiarism checker will check your work against over a billion webpages, published works, and academic papers, so you can be sure of its originality. Find out more about pricing for plagiarism checks here .

A good grade is closer than you think

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Essay Checker

With Ginger’s Essay Checker, correcting common writing errors is easier than ever. Try it free online!

Avoid Common Writing Mistakes with the World’s Top Essay Checker

The Ginger Essay Checker helps you write better papers instantly. Upload as much text as you want – even entire documents – and Essay Checker will automatically correct any spelling mistakes, grammar mistakes, and misused words. Ginger Essay Checker uses patent-pending technology to fix essays, improving your writing just like a human editor would. Take advantage of the most advanced essay corrector on the market. You’ll benefit from instant proofreading, plus you’ll automatically improve your writing skills as you view highlighted errors side by side with Ginger Essay Checker’s corrections.

Check Essays Fast with Ginger Software

You’ve selected a topic, constructed an outline, written your thesis statement, and completed your first draft. Don’t let your efforts go to waste. With Ginger Software’s Essay Checker, you’ll be the only one to see those little mistakes and perhaps even those glaring errors peppering your paper. The tedious task of checking an essay once had to be done by hand – and proofreading sometimes added hours of work to large projects. Where writers once had to rely on peers or editors to spot and correct mistakes, Essay Checker has taken over. Better yet, this innovative online paper checker does what other free essay corrector programs can’t do: Not only does it flag errors so you can learn from your mistakes, it automatically corrects all spelling and grammar issues at lightning speed.

Stop Wasting Time and Effort Checking Papers

You have a heavy workload, and the last thing you need to do is waste time staring at an essay you’ve just spent hours writing. Proofreading your own work – especially when you’re tired – allows you to find a few mistakes, but some errors inevitably go unnoticed no matter how much time you spend re-reading what you’ve just written. The Ginger Essay Checker lightens your workload by completely eliminating the need for hours of tedious self-review. With Ginger’s groundbreaking Essay Checker, a vast array of grammar mistakes and spelling errors are detected and corrected with unmatched accuracy. While most online paper checker tools claiming to correct essays simply flag mistakes and sometimes make suggestions for fixing them, Essay Checker goes above and beyond, picking up on such issues as tense usage errors, singular vs. plural errors, and more. Even the most sophisticated sentence structures are checked with accuracy, ensuring no mistake is overlooked even though all you’ve done is made a single click.

Essay Checker Paves the Way to Writing Success

Writing has always been important, and accuracy has always been sought after. Getting your spelling, grammar, and syntax right matters, whether your audience is online or off. Error-free writing is a vital skill in the academic world, and it’s just as important for conducting business. Casual bloggers need to maintain credibility with their audiences, and professional writers burn out fast when faced with mounds of work to proofread. Make sure your message is conveyed with clarity by checking your work before submitting it to readers – no matter who they are.

Checking essays has never been easier. With Ginger Essay Checker, you’ll save time, boost productivity, and make the right impression.

Your path to academic success

Improve your paper with our award-winning Proofreading Services ,  Plagiarism Checker , Citation Generator , AI Detector & Knowledge Base .

Proofreading & Editing

Get expert help from Scribbr’s academic editors, who will proofread and edit your essay, paper, or dissertation to perfection.

Plagiarism Checker

Detect and resolve unintentional plagiarism with the Scribbr Plagiarism Checker, so you can submit your paper with confidence.

Citation Generator

Generate accurate citations with Scribbr’s free citation generator and save hours of repetitive work.

Happy to help you

You’re not alone. Together with our team and highly qualified editors , we help you answer all your questions about academic writing.

Open 24/7 – 365 days a year. Always available to help you.

Very satisfied students

This is our reason for working. We want to make  all students  happy, every day.

Everything you need to write an A-grade paper

Free resources used by 5,000,000 students every month.

Bite-sized videos that guide you through the writing process. Get the popcorn, sit back, and learn!

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Ready-made slides for teachers and professors that want to kickstart their lectures.

• Academic writing
• Citing sources
• Methodology
• Research process
• Dissertation structure
• Language rules

Accessible how-to guides full of examples that help you write a flawless essay, proposal, or dissertation.

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Cite any page or article with a single click right from your browser.

Time-saving templates that you can download and edit in Word or Google Docs.

Help you achieve your academic goals

Whether we’re proofreading and editing , checking for plagiarism or AI content , generating citations, or writing useful Knowledge Base articles , our aim is to support students on their journey to become better academic writers.

We believe that every student should have the right tools for academic success. Free tools like a paraphrasing tool , grammar checker, summarizer and an  AI Proofreader . We pave the way to your academic degree.

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Want to contact us directly? No problem.  We  are always here for you.

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Our team helps students graduate by offering:

• A world-class citation generator
• Plagiarism Checker software powered by Turnitin
• Innovative Citation Checker software
• Professional proofreading services
• Over 300 helpful articles about academic writing, citing sources, plagiarism, and more

Scribbr specializes in editing study-related documents . We proofread:

• PhD dissertations
• Research proposals
• Personal statements
• Admission essays
• Motivation letters
• Reflection papers
• Journal articles
• Capstone projects

Scribbr’s Plagiarism Checker is powered by elements of Turnitin’s Similarity Checker , namely the plagiarism detection software and the Internet Archive and Premium Scholarly Publications content databases .

The add-on AI detector is powered by Scribbr’s proprietary software.

The Scribbr Citation Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github .

IELTS Writing Task 2 Essay Checker

Instantly and precisely evaluate your task 2 essay with detailed feedback

(Looking for writing task 1 report checker / writing task 1 letter checker ?)

Click here to explore thousands of task 2 essays written by our users

Overall Band Score

Task response, coherence & cohesion, lexical resource, grammatical range & accuracy, improved naturalness comparison, enhanced essay comparison.

Disclaimer This tool should be seen as a guide rather than a definitive score. Just like human reviewers, AI can be subjective, and the score provided may be accurate within 75%-95% when compared with an official IELTS score. Use this tool to complement your study, but not as a substitute for professional assessment or official IELTS grading.

Introducing the Ultimate IELTS Writing Task 2 Essay Checker: Instant, Accurate, and Free!

Say hello to our cutting-edge, AI-driven IELTS Writing Task 2 Essay Checker, designed to transform your test preparation experience! This innovative online tool provides instant and free correction and evaluation of your IELTS essays, ensuring you’re on the right track to success.

Our advanced AI technology meticulously assesses your writing, delivering comprehensive feedback and invaluable insights to help you excel in both IELTS Academic and General Training. With this powerful assessment tool at your fingertips, you can confidently hone your writing skills and achieve your desired IELTS score.

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146 comments on “AI-Powered IELTS Writing Task 2 Essay Checker (Free & Fast)”

Mam dobara check kar dain

I paid for twice but i didnt acces to premium why?

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Account payments issues

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Plz check my.paper

I expected 8🥲

Writing task 2

The number of vehicle increases is often the reason for the traffic jams phenomenon. This leads to state contributions in taking the necessary actions. It places a strict road fee to limit this problem. This essay agrees that the decision to impose fines is good and it may work because some people will prefer to take voluntary streets and save money for other things. As well, the busy road will be used for emergency situations.

The low-class and middle-class citizens prioritize their personal and family needs. These people are unable to spend their money on something not helpful and incurred due to mistakes like fees. Residents with low income have a plan for their consumption because their financial situation is limited. So that they are highly aware of fines and replace them with free ones. Thus, the busy road will become less congested with cars. For example, in London’s government, they make this action resulting in the distribution of people between voluntary roads and highways.

Making measures to decrease traffic is an appropriate way to make way for emergency situations. The ambulance should be fast to aid people in accidents and using the highway is faster if there are fewer cars. Moreover, there are dangerous wounds that occur at home and need a fast street with low vehicles to move them to healthcare. For instance, Turkey saw a difficult situation for a woman about to give birth but there was extreme traffic jam.

All in all, making a decision about fines on busy roads is an awesome idea to decline the crowded streets because most people will use the free ones and to make way for emergency vehicles to pass quickly.

Plans & Pricing

Check your IELTS essay online

Improve your ielts writing score within two weeks.

41,394 students have used our tool to improve their band scores without paying for expensive tutoring. The service checks your IELTS essay in seconds.

The best new way to check your essay.

Why you will love it, get a band score online, find ideas quickly, improve ielts writing grammar, money back guarantee.

Achieving my dream score seemed impossible until I found this site. The detailed feedback on essays was crucial.

Gill Avneet Kaur

IELTS Writing Result: 7.5

What it can edit?

Writing9 scans your text for all types of mistakes, from typos to sentence structure problems and beyond.

Perfect evaluation

Hundreds of algorithms will assess your writing according to 4 evaluation criteria. Writing9 helps you find the weak points of your essay and make it flawless.

Helpful hints

After you write your essay, you will get helpful tips showing you how to make your essay better. So you always get a band score above 7.

Topic Ideas & Vocabulary Boost

Get ideas and useful words for your essay topic. Make your text more interesting and show off your vocabulary. Your essay will shine!

How does it work?

Type or paste your essay, press the 'check essay' button, get a band score instantly, amazing. right, what people say about writing9.

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• Unlimited checking of essays
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• Advanced grammar checker
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• IELTS Speaking Simulator
• E-Book "The ultimate guide to get a target band score of 7+"
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The instant feedback feature is a game-changer. It pinpointed my mistakes and drastically improved my skills.

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IELTS Writing Result: 7

There's really no risk in your purchase!

Is there a free trial, who corrects my essays, can i trust the service, what tasks can i check, how often can i use your service to check charts, letters and essays, can i check an essay on my own topic, will my writing checking result be similar to the score i will get on the ielts exam, can your service help improve my writing skills overall, start checking your ielts essays today.

SmartMarq: Human and AI Essay marking

Easily manage the essay marking process, then use modern machine learning models to serve as a second or third rater, reducing costs and timelines.

Define Rubrics

Create your scoring rubrics and performance descriptors

Manage Raters

Assign essays to be scored, then view results

Gather Ratings

Raters can easily move through and leave scores and comments

Auto-Scoring

Implement automated essay scoring to flag unusual scores

SmartMarq will streamline your essay marking process

SmartMarq makes it easy to implement large-scale, professional essay scoring.

• Reduce timelines for marking
• Increase convenience by managing fully online
• Implement business rules to ensure quality
• Once raters are done, run the results through our AI to train a custom machine learning model for your data, obtaining a second “rater.”

Note that our powerful AI scoring is customized, specific to each one of your prompts and rubrics – not developed with a shotgun approach based on general heuristics.

Fully integrated into our FastTest ecosystem

We pride ourselves on providing a single ecosystem with configurable modules that covers the entire test development and delivery cycle.  SmartMarq is available both standalone, and as part of our online delivery platform. If you have open-response items, especially extended constructed response (ECR) items, our platforms will improve the process needed to mark these items.  Leverage our user-friendly, highly scalable online marking module to manage hundreds (or just a few) raters, with single or multi-marking situations.

“FastTest reduced the workhours needed to mark our student essays by approximately 60%, cutting it from a multi-day district-wide project to a single day!”

 A K-12 FastTest Client

Manage Users

Upload users and manage assignments to groups of students

Create Rubrics

Create your rubrics, including point values and descriptor

Tag Rubrics to Items

When authoring items, simply assign the rubrics you want to use

Set Marking Rules

Require multiple markers, adjudication of disagreements, and visibility limitations? Users can be specified to see only THEIR students, or have the entire population anonymized and randomized. Configure as you need.

Deliver tests online

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An automated essay scoring systems: a systematic literature review

• Published: 23 September 2021
• Volume 55 , pages 2495–2527, ( 2022 )

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• Dadi Ramesh   ORCID: orcid.org/0000-0002-3967-8914 1 , 2 &
• Suresh Kumar Sanampudi 3  

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Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.

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1 Introduction

Due to COVID 19 outbreak, an online educational system has become inevitable. In the present scenario, almost all the educational institutions ranging from schools to colleges adapt the online education system. The assessment plays a significant role in measuring the learning ability of the student. Most automated evaluation is available for multiple-choice questions, but assessing short and essay answers remain a challenge. The education system is changing its shift to online-mode, like conducting computer-based exams and automatic evaluation. It is a crucial application related to the education domain, which uses natural language processing (NLP) and Machine Learning techniques. The evaluation of essays is impossible with simple programming languages and simple techniques like pattern matching and language processing. Here the problem is for a single question, we will get more responses from students with a different explanation. So, we need to evaluate all the answers concerning the question.

Automated essay scoring (AES) is a computer-based assessment system that automatically scores or grades the student responses by considering appropriate features. The AES research started in 1966 with the Project Essay Grader (PEG) by Ajay et al. ( 1973 ). PEG evaluates the writing characteristics such as grammar, diction, construction, etc., to grade the essay. A modified version of the PEG by Shermis et al. ( 2001 ) was released, which focuses on grammar checking with a correlation between human evaluators and the system. Foltz et al. ( 1999 ) introduced an Intelligent Essay Assessor (IEA) by evaluating content using latent semantic analysis to produce an overall score. Powers et al. ( 2002 ) proposed E-rater and Intellimetric by Rudner et al. ( 2006 ) and Bayesian Essay Test Scoring System (BESTY) by Rudner and Liang ( 2002 ), these systems use natural language processing (NLP) techniques that focus on style and content to obtain the score of an essay. The vast majority of the essay scoring systems in the 1990s followed traditional approaches like pattern matching and a statistical-based approach. Since the last decade, the essay grading systems started using regression-based and natural language processing techniques. AES systems like Dong et al. ( 2017 ) and others developed from 2014 used deep learning techniques, inducing syntactic and semantic features resulting in better results than earlier systems.

Ohio, Utah, and most US states are using AES systems in school education, like Utah compose tool, Ohio standardized test (an updated version of PEG), evaluating millions of student's responses every year. These systems work for both formative, summative assessments and give feedback to students on the essay. Utah provided basic essay evaluation rubrics (six characteristics of essay writing): Development of ideas, organization, style, word choice, sentence fluency, conventions. Educational Testing Service (ETS) has been conducting significant research on AES for more than a decade and designed an algorithm to evaluate essays on different domains and providing an opportunity for test-takers to improve their writing skills. In addition, they are current research content-based evaluation.

The evaluation of essay and short answer scoring should consider the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge. Proper assessment of the parameters mentioned above defines the accuracy of the evaluation system. But all these parameters cannot play an equal role in essay scoring and short answer scoring. In a short answer evaluation, domain knowledge is required, like the meaning of "cell" in physics and biology is different. And while evaluating essays, the implementation of ideas with respect to prompt is required. The system should also assess the completeness of the responses and provide feedback.

Several studies examined AES systems, from the initial to the latest AES systems. In which the following studies on AES systems are Blood ( 2011 ) provided a literature review from PEG 1984–2010. Which has covered only generalized parts of AES systems like ethical aspects, the performance of the systems. Still, they have not covered the implementation part, and it’s not a comparative study and has not discussed the actual challenges of AES systems.

Burrows et al. ( 2015 ) Reviewed AES systems on six dimensions like dataset, NLP techniques, model building, grading models, evaluation, and effectiveness of the model. They have not covered feature extraction techniques and challenges in features extractions. Covered only Machine Learning models but not in detail. This system not covered the comparative analysis of AES systems like feature extraction, model building, and level of relevance, cohesion, and coherence not covered in this review.

Ke et al. ( 2019 ) provided a state of the art of AES system but covered very few papers and not listed all challenges, and no comparative study of the AES model. On the other hand, Hussein et al. in ( 2019 ) studied two categories of AES systems, four papers from handcrafted features for AES systems, and four papers from the neural networks approach, discussed few challenges, and did not cover feature extraction techniques, the performance of AES models in detail.

Klebanov et al. ( 2020 ). Reviewed 50 years of AES systems, listed and categorized all essential features that need to be extracted from essays. But not provided a comparative analysis of all work and not discussed the challenges.

This paper aims to provide a systematic literature review (SLR) on automated essay grading systems. An SLR is an Evidence-based systematic review to summarize the existing research. It critically evaluates and integrates all relevant studies' findings and addresses the research domain's specific research questions. Our research methodology uses guidelines given by Kitchenham et al. ( 2009 ) for conducting the review process; provide a well-defined approach to identify gaps in current research and to suggest further investigation.

We addressed our research method, research questions, and the selection process in Sect.  2 , and the results of the research questions have discussed in Sect.  3 . And the synthesis of all the research questions addressed in Sect.  4 . Conclusion and possible future work discussed in Sect.  5 .

2 Research method

We framed the research questions with PICOC criteria.

Population (P) Student essays and answers evaluation systems.

Intervention (I) evaluation techniques, data sets, features extraction methods.

Comparison (C) Comparison of various approaches and results.

Outcomes (O) Estimate the accuracy of AES systems,

Context (C) NA.

2.1 Research questions

To collect and provide research evidence from the available studies in the domain of automated essay grading, we framed the following research questions (RQ):

RQ1 what are the datasets available for research on automated essay grading?

The answer to the question can provide a list of the available datasets, their domain, and access to the datasets. It also provides a number of essays and corresponding prompts.

RQ2 what are the features extracted for the assessment of essays?

The answer to the question can provide an insight into various features so far extracted, and the libraries used to extract those features.

RQ3, which are the evaluation metrics available for measuring the accuracy of algorithms?

The answer will provide different evaluation metrics for accurate measurement of each Machine Learning approach and commonly used measurement technique.

RQ4 What are the Machine Learning techniques used for automatic essay grading, and how are they implemented?

It can provide insights into various Machine Learning techniques like regression models, classification models, and neural networks for implementing essay grading systems. The response to the question can give us different assessment approaches for automated essay grading systems.

RQ5 What are the challenges/limitations in the current research?

The answer to the question provides limitations of existing research approaches like cohesion, coherence, completeness, and feedback.

2.2 Search process

We conducted an automated search on well-known computer science repositories like ACL, ACM, IEEE Explore, Springer, and Science Direct for an SLR. We referred to papers published from 2010 to 2020 as much of the work during these years focused on advanced technologies like deep learning and natural language processing for automated essay grading systems. Also, the availability of free data sets like Kaggle (2012), Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) by Yannakoudakis et al. ( 2011 ) led to research this domain.

Search Strings : We used search strings like “Automated essay grading” OR “Automated essay scoring” OR “short answer scoring systems” OR “essay scoring systems” OR “automatic essay evaluation” and searched on metadata.

2.3 Selection criteria

After collecting all relevant documents from the repositories, we prepared selection criteria for inclusion and exclusion of documents. With the inclusion and exclusion criteria, it becomes more feasible for the research to be accurate and specific.

Inclusion criteria 1 Our approach is to work with datasets comprise of essays written in English. We excluded the essays written in other languages.

Inclusion criteria 2  We included the papers implemented on the AI approach and excluded the traditional methods for the review.

Inclusion criteria 3 The study is on essay scoring systems, so we exclusively included the research carried out on only text data sets rather than other datasets like image or speech.

Exclusion criteria  We removed the papers in the form of review papers, survey papers, and state of the art papers.

2.4 Quality assessment

In addition to the inclusion and exclusion criteria, we assessed each paper by quality assessment questions to ensure the article's quality. We included the documents that have clearly explained the approach they used, the result analysis and validation.

The quality checklist questions are framed based on the guidelines from Kitchenham et al. ( 2009 ). Each quality assessment question was graded as either 1 or 0. The final score of the study range from 0 to 3. A cut off score for excluding a study from the review is 2 points. Since the papers scored 2 or 3 points are included in the final evaluation. We framed the following quality assessment questions for the final study.

Quality Assessment 1: Internal validity.

Quality Assessment 2: External validity.

Quality Assessment 3: Bias.

The two reviewers review each paper to select the final list of documents. We used the Quadratic Weighted Kappa score to measure the final agreement between the two reviewers. The average resulted from the kappa score is 0.6942, a substantial agreement between the reviewers. The result of evolution criteria shown in Table 1 . After Quality Assessment, the final list of papers for review is shown in Table 2 . The complete selection process is shown in Fig. 1 . The total number of selected papers in year wise as shown in Fig. 2 .

Selection process

Year wise publications

3.1 What are the datasets available for research on automated essay grading?

To work with problem statement especially in Machine Learning and deep learning domain, we require considerable amount of data to train the models. To answer this question, we listed all the data sets used for training and testing for automated essay grading systems. The Cambridge Learner Corpus-First Certificate in English exam (CLC-FCE) Yannakoudakis et al. ( 2011 ) developed corpora that contain 1244 essays and ten prompts. This corpus evaluates whether a student can write the relevant English sentences without any grammatical and spelling mistakes. This type of corpus helps to test the models built for GRE and TOFEL type of exams. It gives scores between 1 and 40.

Bailey and Meurers ( 2008 ), Created a dataset (CREE reading comprehension) for language learners and automated short answer scoring systems. The corpus consists of 566 responses from intermediate students. Mohler and Mihalcea ( 2009 ). Created a dataset for the computer science domain consists of 630 responses for data structure assignment questions. The scores are range from 0 to 5 given by two human raters.

Dzikovska et al. ( 2012 ) created a Student Response Analysis (SRA) corpus. It consists of two sub-groups: the BEETLE corpus consists of 56 questions and approximately 3000 responses from students in the electrical and electronics domain. The second one is the SCIENTSBANK(SemEval-2013) (Dzikovska et al. 2013a ; b ) corpus consists of 10,000 responses on 197 prompts on various science domains. The student responses ladled with "correct, partially correct incomplete, Contradictory, Irrelevant, Non-domain."

In the Kaggle (2012) competition, released total 3 types of corpuses on an Automated Student Assessment Prize (ASAP1) (“ https://www.kaggle.com/c/asap-sas/ ” ) essays and short answers. It has nearly 17,450 essays, out of which it provides up to 3000 essays for each prompt. It has eight prompts that test 7th to 10th grade US students. It gives scores between the [0–3] and [0–60] range. The limitations of these corpora are: (1) it has a different score range for other prompts. (2) It uses statistical features such as named entities extraction and lexical features of words to evaluate essays. ASAP +  + is one more dataset from Kaggle. It is with six prompts, and each prompt has more than 1000 responses total of 10,696 from 8th-grade students. Another corpus contains ten prompts from science, English domains and a total of 17,207 responses. Two human graders evaluated all these responses.

Correnti et al. ( 2013 ) created a Response-to-Text Assessment (RTA) dataset used to check student writing skills in all directions like style, mechanism, and organization. 4–8 grade students give the responses to RTA. Basu et al. ( 2013 ) created a power grading dataset with 700 responses for ten different prompts from US immigration exams. It contains all short answers for assessment.

The TOEFL11 corpus Blanchard et al. ( 2013 ) contains 1100 essays evenly distributed over eight prompts. It is used to test the English language skills of a candidate attending the TOFEL exam. It scores the language proficiency of a candidate as low, medium, and high.

International Corpus of Learner English (ICLE) Granger et al. ( 2009 ) built a corpus of 3663 essays covering different dimensions. It has 12 prompts with 1003 essays that test the organizational skill of essay writing, and13 prompts, each with 830 essays that examine the thesis clarity and prompt adherence.

Argument Annotated Essays (AAE) Stab and Gurevych ( 2014 ) developed a corpus that contains 102 essays with 101 prompts taken from the essayforum2 site. It tests the persuasive nature of the student essay. The SCIENTSBANK corpus used by Sakaguchi et al. ( 2015 ) available in git-hub, containing 9804 answers to 197 questions in 15 science domains. Table 3 illustrates all datasets related to AES systems.

3.2 RQ2 what are the features extracted for the assessment of essays?

Features play a major role in the neural network and other supervised Machine Learning approaches. The automatic essay grading systems scores student essays based on different types of features, which play a prominent role in training the models. Based on their syntax and semantics and they are categorized into three groups. 1. statistical-based features Contreras et al. ( 2018 ); Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ) 2. Style-based (Syntax) features Cummins et al. ( 2016 ); Darwish and Mohamed ( 2020 ); Ke et al. ( 2019 ). 3. Content-based features Dong et al. ( 2017 ). A good set of features appropriate models evolved better AES systems. The vast majority of the researchers are using regression models if features are statistical-based. For Neural Networks models, researches are using both style-based and content-based features. The following table shows the list of various features used in existing AES Systems. Table 4 represents all set of features used for essay grading.

We studied all the feature extracting NLP libraries as shown in Fig. 3 . that are used in the papers. The NLTK is an NLP tool used to retrieve statistical features like POS, word count, sentence count, etc. With NLTK, we can miss the essay's semantic features. To find semantic features Word2Vec Mikolov et al. ( 2013 ), GloVe Jeffrey Pennington et al. ( 2014 ) is the most used libraries to retrieve the semantic text from the essays. And in some systems, they directly trained the model with word embeddings to find the score. From Fig. 4 as observed that non-content-based feature extraction is higher than content-based.

Usages of tools

Number of papers on content based features

3.3 RQ3 which are the evaluation metrics available for measuring the accuracy of algorithms?

The majority of the AES systems are using three evaluation metrics. They are (1) quadrated weighted kappa (QWK) (2) Mean Absolute Error (MAE) (3) Pearson Correlation Coefficient (PCC) Shehab et al. ( 2016 ). The quadratic weighted kappa will find agreement between human evaluation score and system evaluation score and produces value ranging from 0 to 1. And the Mean Absolute Error is the actual difference between human-rated score to system-generated score. The mean square error (MSE) measures the average squares of the errors, i.e., the average squared difference between the human-rated and the system-generated scores. MSE will always give positive numbers only. Pearson's Correlation Coefficient (PCC) finds the correlation coefficient between two variables. It will provide three values (0, 1, − 1). "0" represents human-rated and system scores that are not related. "1" represents an increase in the two scores. "− 1" illustrates a negative relationship between the two scores.

3.4 RQ4 what are the Machine Learning techniques being used for automatic essay grading, and how are they implemented?

After scrutinizing all documents, we categorize the techniques used in automated essay grading systems into four baskets. 1. Regression techniques. 2. Classification model. 3. Neural networks. 4. Ontology-based approach.

All the existing AES systems developed in the last ten years employ supervised learning techniques. Researchers using supervised methods viewed the AES system as either regression or classification task. The goal of the regression task is to predict the score of an essay. The classification task is to classify the essays belonging to (low, medium, or highly) relevant to the question's topic. Since the last three years, most AES systems developed made use of the concept of the neural network.

3.4.1 Regression based models

Mohler and Mihalcea ( 2009 ). proposed text-to-text semantic similarity to assign a score to the student essays. There are two text similarity measures like Knowledge-based measures, corpus-based measures. There eight knowledge-based tests with all eight models. They found the similarity. The shortest path similarity determines based on the length, which shortest path between two contexts. Leacock & Chodorow find the similarity based on the shortest path's length between two concepts using node-counting. The Lesk similarity finds the overlap between the corresponding definitions, and Wu & Palmer algorithm finds similarities based on the depth of two given concepts in the wordnet taxonomy. Resnik, Lin, Jiang&Conrath, Hirst& St-Onge find the similarity based on different parameters like the concept, probability, normalization factor, lexical chains. In corpus-based likeness, there LSA BNC, LSA Wikipedia, and ESA Wikipedia, latent semantic analysis is trained on Wikipedia and has excellent domain knowledge. Among all similarity scores, correlation scores LSA Wikipedia scoring accuracy is more. But these similarity measure algorithms are not using NLP concepts. These models are before 2010 and basic concept models to continue the research automated essay grading with updated algorithms on neural networks with content-based features.

Adamson et al. ( 2014 ) proposed an automatic essay grading system which is a statistical-based approach in this they retrieved features like POS, Character count, Word count, Sentence count, Miss spelled words, n-gram representation of words to prepare essay vector. They formed a matrix with these all vectors in that they applied LSA to give a score to each essay. It is a statistical approach that doesn’t consider the semantics of the essay. The accuracy they got when compared to the human rater score with the system is 0.532.

Cummins et al. ( 2016 ). Proposed Timed Aggregate Perceptron vector model to give ranking to all the essays, and later they converted the rank algorithm to predict the score of the essay. The model trained with features like Word unigrams, bigrams, POS, Essay length, grammatical relation, Max word length, sentence length. It is multi-task learning, gives ranking to the essays, and predicts the score for the essay. The performance evaluated through QWK is 0.69, a substantial agreement between the human rater and the system.

Sultan et al. ( 2016 ). Proposed a Ridge regression model to find short answer scoring with Question Demoting. Question Demoting is the new concept included in the essay's final assessment to eliminate duplicate words from the essay. The extracted features are Text Similarity, which is the similarity between the student response and reference answer. Question Demoting is the number of repeats in a student response. With inverse document frequency, they assigned term weight. The sentence length Ratio is the number of words in the student response, is another feature. With these features, the Ridge regression model was used, and the accuracy they got 0.887.

Contreras et al. ( 2018 ). Proposed Ontology based on text mining in this model has given a score for essays in phases. In phase-I, they generated ontologies with ontoGen and SVM to find the concept and similarity in the essay. In phase II from ontologies, they retrieved features like essay length, word counts, correctness, vocabulary, and types of word used, domain information. After retrieving statistical data, they used a linear regression model to find the score of the essay. The accuracy score is the average of 0.5.

Darwish and Mohamed ( 2020 ) proposed the fusion of fuzzy Ontology with LSA. They retrieve two types of features, like syntax features and semantic features. In syntax features, they found Lexical Analysis with tokens, and they construct a parse tree. If the parse tree is broken, the essay is inconsistent—a separate grade assigned to the essay concerning syntax features. The semantic features are like similarity analysis, Spatial Data Analysis. Similarity analysis is to find duplicate sentences—Spatial Data Analysis for finding Euclid distance between the center and part. Later they combine syntax features and morphological features score for the final score. The accuracy they achieved with the multiple linear regression model is 0.77, mostly on statistical features.

Süzen Neslihan et al. ( 2020 ) proposed a text mining approach for short answer grading. First, their comparing model answers with student response by calculating the distance between two sentences. By comparing the model answer with student response, they find the essay's completeness and provide feedback. In this approach, model vocabulary plays a vital role in grading, and with this model vocabulary, the grade will be assigned to the student's response and provides feedback. The correlation between the student answer to model answer is 0.81.

3.4.2 Classification based Models

Persing and Ng ( 2013 ) used a support vector machine to score the essay. The features extracted are OS, N-gram, and semantic text to train the model and identified the keywords from the essay to give the final score.

Sakaguchi et al. ( 2015 ) proposed two methods: response-based and reference-based. In response-based scoring, the extracted features are response length, n-gram model, and syntactic elements to train the support vector regression model. In reference-based scoring, features such as sentence similarity using word2vec is used to find the cosine similarity of the sentences that is the final score of the response. First, the scores were discovered individually and later combined two features to find a final score. This system gave a remarkable increase in performance by combining the scores.

Mathias and Bhattacharyya ( 2018a ; b ) Proposed Automated Essay Grading Dataset with Essay Attribute Scores. The first concept features selection depends on the essay type. So the common attributes are Content, Organization, Word Choice, Sentence Fluency, Conventions. In this system, each attribute is scored individually, with the strength of each attribute identified. The model they used is a random forest classifier to assign scores to individual attributes. The accuracy they got with QWK is 0.74 for prompt 1 of the ASAS dataset ( https://www.kaggle.com/c/asap-sas/ ).

Ke et al. ( 2019 ) used a support vector machine to find the response score. In this method, features like Agreeability, Specificity, Clarity, Relevance to prompt, Conciseness, Eloquence, Confidence, Direction of development, Justification of opinion, and Justification of importance. First, the individual parameter score obtained was later combined with all scores to give a final response score. The features are used in the neural network to find whether the sentence is relevant to the topic or not.

Salim et al. ( 2019 ) proposed an XGBoost Machine Learning classifier to assess the essays. The algorithm trained on features like word count, POS, parse tree depth, and coherence in the articles with sentence similarity percentage; cohesion and coherence are considered for training. And they implemented K-fold cross-validation for a result the average accuracy after specific validations is 68.12.

3.4.3 Neural network models

Shehab et al. ( 2016 ) proposed a neural network method that used learning vector quantization to train human scored essays. After training, the network can provide a score to the ungraded essays. First, we should process the essay to remove Spell checking and then perform preprocessing steps like Document Tokenization, stop word removal, Stemming, and submit it to the neural network. Finally, the model will provide feedback on the essay, whether it is relevant to the topic. And the correlation coefficient between human rater and system score is 0.7665.

Kopparapu and De ( 2016 ) proposed the Automatic Ranking of Essays using Structural and Semantic Features. This approach constructed a super essay with all the responses. Next, ranking for a student essay is done based on the super-essay. The structural and semantic features derived helps to obtain the scores. In a paragraph, 15 Structural features like an average number of sentences, the average length of sentences, and the count of words, nouns, verbs, adjectives, etc., are used to obtain a syntactic score. A similarity score is used as semantic features to calculate the overall score.

Dong and Zhang ( 2016 ) proposed a hierarchical CNN model. The model builds two layers with word embedding to represents the words as the first layer. The second layer is a word convolution layer with max-pooling to find word vectors. The next layer is a sentence-level convolution layer with max-pooling to find the sentence's content and synonyms. A fully connected dense layer produces an output score for an essay. The accuracy with the hierarchical CNN model resulted in an average QWK of 0.754.

Taghipour and Ng ( 2016 ) proposed a first neural approach for essay scoring build in which convolution and recurrent neural network concepts help in scoring an essay. The network uses a lookup table with the one-hot representation of the word vector of an essay. The final efficiency of the network model with LSTM resulted in an average QWK of 0.708.

Dong et al. ( 2017 ). Proposed an Attention-based scoring system with CNN + LSTM to score an essay. For CNN, the input parameters were character embedding and word embedding, and it has attention pooling layers and used NLTK to obtain word and character embedding. The output gives a sentence vector, which provides sentence weight. After CNN, it will have an LSTM layer with an attention pooling layer, and this final layer results in the final score of the responses. The average QWK score is 0.764.

Riordan et al. ( 2017 ) proposed a neural network with CNN and LSTM layers. Word embedding, given as input to a neural network. An LSTM network layer will retrieve the window features and delivers them to the aggregation layer. The aggregation layer is a superficial layer that takes a correct window of words and gives successive layers to predict the answer's sore. The accuracy of the neural network resulted in a QWK of 0.90.

Zhao et al. ( 2017 ) proposed a new concept called Memory-Augmented Neural network with four layers, input representation layer, memory addressing layer, memory reading layer, and output layer. An input layer represents all essays in a vector form based on essay length. After converting the word vector, the memory addressing layer takes a sample of the essay and weighs all the terms. The memory reading layer takes the input from memory addressing segment and finds the content to finalize the score. Finally, the output layer will provide the final score of the essay. The accuracy of essay scores is 0.78, which is far better than the LSTM neural network.

Mathias and Bhattacharyya ( 2018a ; b ) proposed deep learning networks using LSTM with the CNN layer and GloVe pre-trained word embeddings. For this, they retrieved features like Sentence count essays, word count per sentence, Number of OOVs in the sentence, Language model score, and the text's perplexity. The network predicted the goodness scores of each essay. The higher the goodness scores, means higher the rank and vice versa.

Nguyen and Dery ( 2016 ). Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%.

Ruseti et al. ( 2018 ) proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network with the max-pooling layer molded on the word embedding of each document. It will provide scoring to the essay by comparing it with a summary of the essay from another Bi-GRU network. The result obtained an accuracy of 0.55.

Wang et al. ( 2018a ; b ) proposed an automatic scoring system with the bi-LSTM recurrent neural network model and retrieved the features using the word2vec technique. This method generated word embeddings from the essay words using the skip-gram model. And later, word embedding is used to train the neural network to find the final score. The softmax layer in LSTM obtains the importance of each word. This method used a QWK score of 0.83%.

Dasgupta et al. ( 2018 ) proposed a technique for essay scoring with augmenting textual qualitative Features. It extracted three types of linguistic, cognitive, and psychological features associated with a text document. The linguistic features are Part of Speech (POS), Universal Dependency relations, Structural Well-formedness, Lexical Diversity, Sentence Cohesion, Causality, and Informativeness of the text. The psychological features derived from the Linguistic Information and Word Count (LIWC) tool. They implemented a convolution recurrent neural network that takes input as word embedding and sentence vector, retrieved from the GloVe word vector. And the second layer is the Convolution Layer to find local features. The next layer is the recurrent neural network (LSTM) to find corresponding of the text. The accuracy of this method resulted in an average QWK of 0.764.

Liang et al. ( 2018 ) proposed a symmetrical neural network AES model with Bi-LSTM. They are extracting features from sample essays and student essays and preparing an embedding layer as input. The embedding layer output is transfer to the convolution layer from that LSTM will be trained. Hear the LSRM model has self-features extraction layer, which will find the essay's coherence. The average QWK score of SBLSTMA is 0.801.

Liu et al. ( 2019 ) proposed two-stage learning. In the first stage, they are assigning a score based on semantic data from the essay. The second stage scoring is based on some handcrafted features like grammar correction, essay length, number of sentences, etc. The average score of the two stages is 0.709.

Pedro Uria Rodriguez et al. ( 2019 ) proposed a sequence-to-sequence learning model for automatic essay scoring. They used BERT (Bidirectional Encoder Representations from Transformers), which extracts the semantics from a sentence from both directions. And XLnet sequence to sequence learning model to extract features like the next sentence in an essay. With this pre-trained model, they attained coherence from the essay to give the final score. The average QWK score of the model is 75.5.

Xia et al. ( 2019 ) proposed a two-layer Bi-directional LSTM neural network for the scoring of essays. The features extracted with word2vec to train the LSTM and accuracy of the model in an average of QWK is 0.870.

Kumar et al. ( 2019 ) Proposed an AutoSAS for short answer scoring. It used pre-trained Word2Vec and Doc2Vec models trained on Google News corpus and Wikipedia dump, respectively, to retrieve the features. First, they tagged every word POS and they found weighted words from the response. It also found prompt overlap to observe how the answer is relevant to the topic, and they defined lexical overlaps like noun overlap, argument overlap, and content overlap. This method used some statistical features like word frequency, difficulty, diversity, number of unique words in each response, type-token ratio, statistics of the sentence, word length, and logical operator-based features. This method uses a random forest model to train the dataset. The data set has sample responses with their associated score. The model will retrieve the features from both responses like graded and ungraded short answers with questions. The accuracy of AutoSAS with QWK is 0.78. It will work on any topics like Science, Arts, Biology, and English.

Jiaqi Lun et al. ( 2020 ) proposed an automatic short answer scoring with BERT. In this with a reference answer comparing student responses and assigning scores. The data augmentation is done with a neural network and with one correct answer from the dataset classifying reaming responses as correct or incorrect.

Zhu and Sun ( 2020 ) proposed a multimodal Machine Learning approach for automated essay scoring. First, they count the grammar score with the spaCy library and numerical count as the number of words and sentences with the same library. With this input, they trained a single and Bi LSTM neural network for finding the final score. For the LSTM model, they prepared sentence vectors with GloVe and word embedding with NLTK. Bi-LSTM will check each sentence in both directions to find semantic from the essay. The average QWK score with multiple models is 0.70.

3.4.4 Ontology based approach

Mohler et al. ( 2011 ) proposed a graph-based method to find semantic similarity in short answer scoring. For the ranking of answers, they used the support vector regression model. The bag of words is the main feature extracted in the system.

Ramachandran et al. ( 2015 ) also proposed a graph-based approach to find lexical based semantics. Identified phrase patterns and text patterns are the features to train a random forest regression model to score the essays. The accuracy of the model in a QWK is 0.78.

Zupanc et al. ( 2017 ) proposed sentence similarity networks to find the essay's score. Ajetunmobi and Daramola ( 2017 ) recommended an ontology-based information extraction approach and domain-based ontology to find the score.

3.4.5 Speech response scoring

Automatic scoring is in two ways one is text-based scoring, other is speech-based scoring. This paper discussed text-based scoring and its challenges, and now we cover speech scoring and common points between text and speech-based scoring. Evanini and Wang ( 2013 ), Worked on speech scoring of non-native school students, extracted features with speech ratter, and trained a linear regression model, concluding that accuracy varies based on voice pitching. Loukina et al. ( 2015 ) worked on feature selection from speech data and trained SVM. Malinin et al. ( 2016 ) used neural network models to train the data. Loukina et al. ( 2017 ). Proposed speech and text-based automatic scoring. Extracted text-based features, speech-based features and trained a deep neural network for speech-based scoring. They extracted 33 types of features based on acoustic signals. Malinin et al. ( 2017 ). Wu Xixin et al. ( 2020 ) Worked on deep neural networks for spoken language assessment. Incorporated different types of models and tested them. Ramanarayanan et al. ( 2017 ) worked on feature extraction methods and extracted punctuation, fluency, and stress and trained different Machine Learning models for scoring. Knill et al. ( 2018 ). Worked on Automatic speech recognizer and its errors how its impacts the speech assessment.

3.4.5.1 The state of the art

This section provides an overview of the existing AES systems with a comparative study w. r. t models, features applied, datasets, and evaluation metrics used for building the automated essay grading systems. We divided all 62 papers into two sets of the first set of review papers in Table 5 with a comparative study of the AES systems.

3.4.6 Comparison of all approaches

In our study, we divided major AES approaches into three categories. Regression models, classification models, and neural network models. The regression models failed to find cohesion and coherence from the essay because it trained on BoW(Bag of Words) features. In processing data from input to output, the regression models are less complicated than neural networks. There are unable to find many intricate patterns from the essay and unable to find sentence connectivity. If we train the model with BoW features in the neural network approach, the model never considers the essay's coherence and coherence.

First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. We can form a vector with BoW and Word2vec, TF-IDF. The BoW and Word2vec vector representation of essays represented in Table 6 . The vector representation of BoW with TF-IDF is not incorporating the essays semantic, and it’s just statistical learning from a given vector. Word2vec vector comprises semantic of essay in a unidirectional way.

In BoW, the vector contains the frequency of word occurrences in the essay. The vector represents 1 and more based on the happenings of words in the essay and 0 for not present. So, in BoW, the vector does not maintain the relationship with adjacent words; it’s just for single words. In word2vec, the vector represents the relationship between words with other words and sentences prompt in multiple dimensional ways. But word2vec prepares vectors in a unidirectional way, not in a bidirectional way; word2vec fails to find semantic vectors when a word has two meanings, and the meaning depends on adjacent words. Table 7 represents a comparison of Machine Learning models and features extracting methods.

In AES, cohesion and coherence will check the content of the essay concerning the essay prompt these can be extracted from essay in the vector from. Two more parameters are there to access an essay is completeness and feedback. Completeness will check whether student’s response is sufficient or not though the student wrote correctly. Table 8 represents all four parameters comparison for essay grading. Table 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

3.5 What are the challenges/limitations in the current research?

From our study and results discussed in the previous sections, many researchers worked on automated essay scoring systems with numerous techniques. We have statistical methods, classification methods, and neural network approaches to evaluate the essay automatically. The main goal of the automated essay grading system is to reduce human effort and improve consistency.

The vast majority of essay scoring systems are dealing with the efficiency of the algorithm. But there are many challenges in automated essay grading systems. One should assess the essay by following parameters like the relevance of the content to the prompt, development of ideas, Cohesion, Coherence, and domain knowledge.

No model works on the relevance of content, which means whether student response or explanation is relevant to the given prompt or not if it is relevant to how much it is appropriate, and there is no discussion about the cohesion and coherence of the essays. All researches concentrated on extracting the features using some NLP libraries, trained their models, and testing the results. But there is no explanation in the essay evaluation system about consistency and completeness, But Palma and Atkinson ( 2018 ) explained coherence-based essay evaluation. And Zupanc and Bosnic ( 2014 ) also used the word coherence to evaluate essays. And they found consistency with latent semantic analysis (LSA) for finding coherence from essays, but the dictionary meaning of coherence is "The quality of being logical and consistent."

Another limitation is there is no domain knowledge-based evaluation of essays using Machine Learning models. For example, the meaning of a cell is different from biology to physics. Many Machine Learning models extract features with WordVec and GloVec; these NLP libraries cannot convert the words into vectors when they have two or more meanings.

3.5.1 Other challenges that influence the Automated Essay Scoring Systems.

All these approaches worked to improve the QWK score of their models. But QWK will not assess the model in terms of features extraction and constructed irrelevant answers. The QWK is not evaluating models whether the model is correctly assessing the answer or not. There are many challenges concerning students' responses to the Automatic scoring system. Like in evaluating approach, no model has examined how to evaluate the constructed irrelevant and adversarial answers. Especially the black box type of approaches like deep learning models provides more options to the students to bluff the automated scoring systems.

The Machine Learning models that work on statistical features are very vulnerable. Based on Powers et al. ( 2001 ) and Bejar Isaac et al. ( 2014 ), the E-rater was failed on Constructed Irrelevant Responses Strategy (CIRS). From the study of Bejar et al. ( 2013 ), Higgins and Heilman ( 2014 ), observed that when student response contain irrelevant content or shell language concurring to prompt will influence the final score of essays in an automated scoring system.

In deep learning approaches, most of the models automatically read the essay's features, and some methods work on word-based embedding and other character-based embedding features. From the study of Riordan Brain et al. ( 2019 ), The character-based embedding systems do not prioritize spelling correction. However, it is influencing the final score of the essay. From the study of Horbach and Zesch ( 2019 ), Various factors are influencing AES systems. For example, there are data set size, prompt type, answer length, training set, and human scorers for content-based scoring.

Ding et al. ( 2020 ) reviewed that the automated scoring system is vulnerable when a student response contains more words from prompt, like prompt vocabulary repeated in the response. Parekh et al. ( 2020 ) and Kumar et al. ( 2020 ) tested various neural network models of AES by iteratively adding important words, deleting unimportant words, shuffle the words, and repeating sentences in an essay and found that no change in the final score of essays. These neural network models failed to recognize common sense in adversaries' essays and give more options for the students to bluff the automated systems.

Other than NLP and ML techniques for AES. From Wresch ( 1993 ) to Madnani and Cahill ( 2018 ). discussed the complexity of AES systems, standards need to be followed. Like assessment rubrics to test subject knowledge, irrelevant responses, and ethical aspects of an algorithm like measuring the fairness of student response.

Fairness is an essential factor for automated systems. For example, in AES, fairness can be measure in an agreement between human score to machine score. Besides this, From Loukina et al. ( 2019 ), the fairness standards include overall score accuracy, overall score differences, and condition score differences between human and system scores. In addition, scoring different responses in the prospect of constructive relevant and irrelevant will improve fairness.

Madnani et al. ( 2017a ; b ). Discussed the fairness of AES systems for constructed responses and presented RMS open-source tool for detecting biases in the models. With this, one can change fairness standards according to their analysis of fairness.

From Berzak et al.'s ( 2018 ) approach, behavior factors are a significant challenge in automated scoring systems. That helps to find language proficiency, word characteristics (essential words from the text), predict the critical patterns from the text, find related sentences in an essay, and give a more accurate score.

Rupp ( 2018 ), has discussed the designing, evaluating, and deployment methodologies for AES systems. They provided notable characteristics of AES systems for deployment. They are like model performance, evaluation metrics for a model, threshold values, dynamically updated models, and framework.

First, we should check the model performance on different datasets and parameters for operational deployment. Selecting Evaluation metrics for AES models are like QWK, correlation coefficient, or sometimes both. Kelley and Preacher ( 2012 ) have discussed three categories of threshold values: marginal, borderline, and acceptable. The values can be varied based on data size, model performance, type of model (single scoring, multiple scoring models). Once a model is deployed and evaluates millions of responses every time for optimal responses, we need a dynamically updated model based on prompt and data. Finally, framework designing of AES model, hear a framework contains prompts where test-takers can write the responses. One can design two frameworks: a single scoring model for a single methodology and multiple scoring models for multiple concepts. When we deploy multiple scoring models, each prompt could be trained separately, or we can provide generalized models for all prompts with this accuracy may vary, and it is challenging.

4 Synthesis

Our Systematic literature review on the automated essay grading system first collected 542 papers with selected keywords from various databases. After inclusion and exclusion criteria, we left with 139 articles; on these selected papers, we applied Quality assessment criteria with two reviewers, and finally, we selected 62 writings for final review.

Our observations on automated essay grading systems from 2010 to 2020 are as followed:

The implementation techniques of automated essay grading systems are classified into four buckets; there are 1. regression models 2. Classification models 3. Neural networks 4. Ontology-based methodology, but using neural networks, the researchers are more accurate than other techniques, and all the methods state of the art provided in Table 3 .

The majority of the regression and classification models on essay scoring used statistical features to find the final score. It means the systems or models trained on such parameters as word count, sentence count, etc. though the parameters extracted from the essay, the algorithm are not directly training on essays. The algorithms trained on some numbers obtained from the essay and hear if numbers matched the composition will get a good score; otherwise, the rating is less. In these models, the evaluation process is entirely on numbers, irrespective of the essay. So, there is a lot of chance to miss the coherence, relevance of the essay if we train our algorithm on statistical parameters.

In the neural network approach, the models trained on Bag of Words (BoW) features. The BoW feature is missing the relationship between a word to word and the semantic meaning of the sentence. E.g., Sentence 1: John killed bob. Sentence 2: bob killed John. In these two sentences, the BoW is "John," "killed," "bob."

In the Word2Vec library, if we are prepared a word vector from an essay in a unidirectional way, the vector will have a dependency with other words and finds the semantic relationship with other words. But if a word has two or more meanings like "Bank loan" and "River Bank," hear bank has two implications, and its adjacent words decide the sentence meaning; in this case, Word2Vec is not finding the real meaning of the word from the sentence.

The features extracted from essays in the essay scoring system are classified into 3 type's features like statistical features, style-based features, and content-based features, which are explained in RQ2 and Table 3 . But statistical features, are playing a significant role in some systems and negligible in some systems. In Shehab et al. ( 2016 ); Cummins et al. ( 2016 ). Dong et al. ( 2017 ). Dong and Zhang ( 2016 ). Mathias and Bhattacharyya ( 2018a ; b ) Systems the assessment is entirely on statistical and style-based features they have not retrieved any content-based features. And in other systems that extract content from the essays, the role of statistical features is for only preprocessing essays but not included in the final grading.

In AES systems, coherence is the main feature to be considered while evaluating essays. The actual meaning of coherence is to stick together. That is the logical connection of sentences (local level coherence) and paragraphs (global level coherence) in a story. Without coherence, all sentences in a paragraph are independent and meaningless. In an Essay, coherence is a significant feature that is explaining everything in a flow and its meaning. It is a powerful feature in AES system to find the semantics of essay. With coherence, one can assess whether all sentences are connected in a flow and all paragraphs are related to justify the prompt. Retrieving the coherence level from an essay is a critical task for all researchers in AES systems.

In automatic essay grading systems, the assessment of essays concerning content is critical. That will give the actual score for the student. Most of the researches used statistical features like sentence length, word count, number of sentences, etc. But according to collected results, 32% of the systems used content-based features for the essay scoring. Example papers which are on content-based assessment are Taghipour and Ng ( 2016 ); Persing and Ng ( 2013 ); Wang et al. ( 2018a , 2018b ); Zhao et al. ( 2017 ); Kopparapu and De ( 2016 ), Kumar et al. ( 2019 ); Mathias and Bhattacharyya ( 2018a ; b ); Mohler and Mihalcea ( 2009 ) are used content and statistical-based features. The results are shown in Fig. 3 . And mainly the content-based features extracted with word2vec NLP library, but word2vec is capable of capturing the context of a word in a document, semantic and syntactic similarity, relation with other terms, but word2vec is capable of capturing the context word in a uni-direction either left or right. If a word has multiple meanings, there is a chance of missing the context in the essay. After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.

On the other hand, Horbach and Zesch ( 2019 ); Riordan Brain et al. ( 2019 ); Ding et al. ( 2020 ); Kumar et al. ( 2020 ) proved that neural network models are vulnerable when a student response contains constructed irrelevant, adversarial answers. And a student can easily bluff an automated scoring system by submitting different responses like repeating sentences and repeating prompt words in an essay. From Loukina et al. ( 2019 ), and Madnani et al. ( 2017b ). The fairness of an algorithm is an essential factor to be considered in AES systems.

While talking about speech assessment, the data set contains audios of duration up to one minute. Feature extraction techniques are entirely different from text assessment, and accuracy varies based on speaking fluency, pitching, male to female voice and boy to adult voice. But the training algorithms are the same for text and speech assessment.

Once an AES system evaluates essays and short answers accurately in all directions, there is a massive demand for automated systems in the educational and related world. Now AES systems are deployed in GRE, TOEFL exams; other than these, we can deploy AES systems in massive open online courses like Coursera(“ https://coursera.org/learn//machine-learning//exam ”), NPTEL ( https://swayam.gov.in/explorer ), etc. still they are assessing student performance with multiple-choice questions. In another perspective, AES systems can be deployed in information retrieval systems like Quora, stack overflow, etc., to check whether the retrieved response is appropriate to the question or not and can give ranking to the retrieved answers.

5 Conclusion and future work

As per our Systematic literature review, we studied 62 papers. There exist significant challenges for researchers in implementing automated essay grading systems. Several researchers are working rigorously on building a robust AES system despite its difficulty in solving this problem. All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based. And 90% of essay grading systems are used Kaggle ASAP (2012) dataset, which has general essays from students and not required any domain knowledge, so there is a need for domain-specific essay datasets to train and test. Feature extraction is with NLTK, WordVec, and GloVec NLP libraries; these libraries have many limitations while converting a sentence into vector form. Apart from feature extraction and training Machine Learning models, no system is accessing the essay's completeness. No system provides feedback to the student response and not retrieving coherence vectors from the essay—another perspective the constructive irrelevant and adversarial student responses still questioning AES systems.

Our proposed research work will go on the content-based assessment of essays with domain knowledge and find a score for the essays with internal and external consistency. And we will create a new dataset concerning one domain. And another area in which we can improve is the feature extraction techniques.

This study includes only four digital databases for study selection may miss some functional studies on the topic. However, we hope that we covered most of the significant studies as we manually collected some papers published in useful journals.

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Ramesh, D., Sanampudi, S.K. An automated essay scoring systems: a systematic literature review. Artif Intell Rev 55 , 2495–2527 (2022). https://doi.org/10.1007/s10462-021-10068-2

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