best essay grading software

Essay Grader Solution

Responsive learning environments typically involve frequent formative assessments in order to gauge how well students are absorbing classroom instruction. In order to handle the corresponding high volume of paper grading, many teachers rely on test grader apps that can expedite the scoring process.

However, in English, history, and other humanities classes that tend to have more essay assignments, oral presentations, and project-based work, teacher-graded rubrics are a more effective approach to evaluating performance and comprehension. That appears to rule out standard bubble form graders for teachers in those subject areas, leaving them without any grading assistance at all.

Scoring with rubrics

Rubrics establish a guide for evaluating the quality of student work. Whether scoring an essay or research paper , a live performance or art project, or other student-constructed responses, rubrics clearly delineate the various components of the assignment to be graded and the degree of success achieved within each of those areas.

These expectations are communicated to the student at the beginning of the assignment and then scored accordingly by the teacher upon its completion. The dilemma that arises is how to simplify and speed up that grading process when score determination must be done directly by the teacher.

ASSESSMENT MADE EASY

Because GradeCam was the brainchild of experienced teachers, creating a solution for handling time-consuming rubric assignments was a priority. Obviously, there is a certain amount of teacher time required to score these assignments that simply can’t be avoided, but there is also a way to streamline this process and save time on the backend.

Rather than using student-completed answer forms like with regular tests, GradeCam allows teachers to create teacher-completed rubric forms that can be quickly and easily filled in using “The Bingo Method” and then scanned and recorded automatically. This speeds up the assignment and transfer of grades, as well as the data generation necessary to review and respond to areas of concern.

Easy Grader Highlights:

Try Gradient Teacher Premium free for 60 days.

Find the solution that’s right for you.

Revolutionize Your Writing Process with Smodin AI Grader: A Smarter Way to get feedback and achieve academic excellence!

For Students

Stay ahead of the curve, with objective feedback and tools to improve your writing.

Your Virtual Tutor

Harness the expertise of a real-time virtual teacher who will guide every paragraph in your writing process, ensuring you produce an A+ masterpiece in a fraction of the time.

Unbiased Evaluation

Ensure an impartial and objective assessment, removing any potential bias or subjectivity that may be an influence in traditional grading methods.

Perfect your assignments

With the “Write with AI” tool, transform your ideas into words with a few simple clicks. Excel at all your essays, assignments, reports etc. and witness your writing skills soar to new heights

For teachers

Revolutionize your Teaching Methods

Spend less on grading

Embrace the power of efficiency and instant feedback with our cutting-edge tool, designed to save you time while providing a fair and unbiased evaluation, delivering consistent and objective feedback.

Reach out to more students

Upload documents in bulk and establish your custom assessment criteria, ensuring a tailored evaluation process. Expand your reach and impact by engaging with more students.

Focus on what you love

Let AI Grading handle the heavy lifting of assessments for you. With its data-driven algorithms and standardized criteria, it takes care of all your grading tasks, freeing up your valuable time to do what you're passionate about: teaching.

Grader Rubrics

Pick the systematic frameworks that work as guidelines for assessing and evaluating the quality, proficiency, and alignment of your work, allowing for consistent and objective grading without any bias.

Analytical Thinking

Originality

Organization

Focus Point

Write with AI

Set your tone and keywords, and generate brilliance through your words

AI Grader Average Deviation from Real Grade

Our AI grader matches human scores 82% of the time* AI Scores are 100% consistent**

Deviation from real grade (10 point scale)

Graph: A dataset of essays were graded by professional graders on a range of 1-10 and cross-referenced against the detailed criteria within the rubric to determine their real scores. Deviation was defined by the variation of scores from the real score. The graph contains an overall score (the average of all criterias) as well as each individual criteria. The criteria are the premade criteria available on Smodin's AI Grader, listed in the graph as column headings. The custom rubrics were made using Smodin's AI Grader custom criteria generator to produce each criteria listed in Smodin's premade criterias (the same criteria as the column headings). The overall score for Smodin Premade Rubrics matched human scores 73% of the time with our advanced AI, while custom rubrics generated by Smodin's custom rubric generator matched human grades 82% of the time with our advanced AI. The average deviation from the real scores for all criteria is shown above.

* Rubrics created using Smodin's AI custom criteria matched human scores 82% of the time on the advanced AI setting. Smodin's premade criteria matched human scores 73% of the time. When the AI score differed from the human scores, 86% of the time the score only differed by 1 point on a 10 point scale.

** The AI grader provides 100% consistency, meaning that the same essay will produce the same score every time it's graded. All grades used in the data were repeated 3 times and produced 100% consistency across all 3 grading attempts.

AI Feedback

Unleash the Power of Personalized Feedback: Elevate Your Writing with the Ultimate Web-based Feedback Tool

Elevate your essay writing skills with Smodin AI Grader, and achieve the success you deserve with Smodin. the ultimate AI-powered essay grader tool. Whether you are a student looking to improve your grades or a teacher looking to provide valuable feedback to your students, Smodin has got you covered. Get objective feedback to improve your essays and excel at writing like never before! Don't miss this opportunity to transform your essay-writing journey and unlock your full potential.

Smodin AI Grader: The Best AI Essay Grader for Writing Improvement

As a teacher or as a student, writing essays can be a daunting task. It takes time, effort, and a lot of attention to detail. But what if there was a tool that could make the process easier? Meet Smodin Ai Grader, the best AI essay grader on the market that provides objective feedback and helps you to improve your writing skills.

Objective Feedback with Smodin - The Best AI Essay Grader

Traditional grading methods can often be subjective, with different teachers providing vastly different grades for the same piece of writing. Smodin eliminates this problem by providing consistent and unbiased feedback, ensuring that all students are evaluated fairly. With advanced algorithms, Smodin can analyze and grade essays in real-time, providing instant feedback on strengths and weaknesses.

Improve Your Writing Skills with Smodin - The Best AI Essay Grader

Smodin can analyze essays quickly and accurately, providing detailed feedback on different aspects of your writing, including structure, grammar, vocabulary, and coherence. By identifying areas that need improvement and providing suggestions on how to make your writing more effective, if Smodin detects that your essay has a weak thesis statement, it will provide suggestions on how to improve it. If it detects that your essay has poor grammar, it will provide suggestions on how to correct the errors. This makes it easier for you to make improvements to your essay and get better grades and become a better writer.

Smodin Ai Grader for Teachers - The Best Essay Analysis Tool

For teachers, Smodin can be a valuable tool for grading essays quickly and efficiently, providing detailed feedback to students, and helping them improve their writing skills. With Smodin Ai Grader, teachers can grade essays in real-time, identify common errors, and provide suggestions on how to correct them.

Smodin Ai Grader for Students - The Best Essay Analysis Tool

For students, Smodin can be a valuable tool for improving your writing skills and getting better grades. By analyzing your essay's strengths and weaknesses, Smodin can help you identify areas that need improvement and provide suggestions on how to make your writing more effective. This can be especially useful for students who are struggling with essay writing and need extra help and guidance.

Increase your productivity - The Best AI Essay Grader

Using Smodin can save you a lot of time and effort. Instead of spending hours grading essays manually or struggling to improve your writing without feedback, you can use Smodin to get instant and objective feedback, allowing you to focus on other important tasks.

Smodin is the best AI essay grader on the market that uses advanced algorithms to provide objective feedback and help improve writing skills. With its ability to analyze essays quickly and accurately, Smodin can help students and teachers alike to achieve better results in essay writing.

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Your AI-Powered Grading System

Vexis is the ultimate grading game changer for educators. we're speeding up the grading process, freeing up your time to focus on teaching. sync with your progress, secure your students' data, and enjoy unbiased, accurate grading every time..

How it works

Upload. grade. share.

Personalized Feedback

Tailored insights for each student, improving their learning journey, unbiased grading, objective assessment, eliminating human bias for a fair grading system, detailed reports, in-depth analysis for each answer, providing a detailed grading breakdown, specialized technology, state-of-the-art tech, revolutionizing the grading process for educators, ocr capabilities, transform scanned answer sheets into digital data effortlessly, free form writing, made for free form checking, understands the context rather than matching the keywords.

Built for productivity

Set up classes as individual projects. enjoy unique functionalities for each class. keep your grading organized and your teaching streamlined with vexis., personalized vex reports, vex reports go beyond traditional evaluations. they offer personalized strategies, pinpointing areas of improvement and providing actionable insights. this empowers students to grow, evolve, and excel, transforming the learning experience into a journey of empowerment..

Cut grading time to 10%

Invest your time in lesson planning and curriculum development, not grading..

Automatic Grading for the Classroom

Start saving time with AutoMark's grading copilot. Trusted by educators to grade assignments and deliver feedback in minutes.

Start grading for free today.

We offer a generous free plan so you can start grading without even entering a credit card.

Copilot eliminates the busy work of grading

• Step 1. Set up your grading criteria by uploading your rubric.
• Step 2. Input student assignments, either manually, by file upload, or by importing from your LMS.
• Step 3. Click grade and watch as AutoMark quickly and accurately provides your students' grades and feedback.

Generate Detailed Learning Insights

• See which concepts your students are most struggling with.
• Identify your students' most common mistakes and view targeted feedback.
• Continuously adapt to your students' strengths and weaknesses.

Streamline Grading

Effortlessly grade assignments with tools that automatically align with educational standards.

FERPA & COPPA Compliant

Our commitment to safety ensures we never collect or store identifiable student data.

AI-Enhanced Feedback

Provide instant, AI-driven feedback to help students learn and improve more effectively.

Start saving time today!

AutoMark makes grading a breeze for teachers while providing instant feedback to students. And guess what? It's absolutely FREE!

Unlimited Access

Enjoy all of AutoMark's features without any hidden costs. Perfect for both teachers and students.

What’s included

• Automated Grading
• Quality Feedback
• Classroom analytics
• Secure and Private

Access Forever, Pay Never

No credit card required.

Frequently asked questions

If you can’t find what you’re looking for, feel free to email our support team. We're always here to help.

How does grading automation work?

AutoMark uses advanced algorithms to grade assignments based on predefined answer keys and rubrics. It also offers partial credits for answers that are partially correct.

Can AutoMark handle different types of assignments?

Yes, AutoMark supports various types of assignments, including multiple-choice questions, short answers, essays, and more.

Can I customize the grading rubric in AutoMark?

Yes, you can customize the grading rubric to fit the specific requirements of your assignments.

Is AutoMark compatible with my school’s learning management system?

Yes, AutoMark is designed to be compatible with many widely-used learning management systems. If you encounter any compatibility issues, our support team will be glad to assist you.

Is my students data secure with AutoMark?

Yes, data security is our top priority. We follow best practices and strict security protocols to ensure the safety and confidentiality of your students’ data.

Can students access AutoMark directly?

Yes, students can access their performance dashboard directly in AutoMark, allowing them to see their grades and feedback.

How does pricing work?

AutoMark offers different pricing tiers based on the number of students and teachers. We also offer special pricing for educational institutions. You can find more details on our Pricing page.

What kind of customer support does AutoMark offer?

We offer 24/7 customer support to ensure all your needs and issues are addressed promptly.

What if I need to connect more students than the limit in my plan?

If you need to connect more students than the limit in your current plan, you can upgrade to a higher plan or contact us for a custom solution.

e-rater ®  Scoring Engine

Evaluates students’ writing proficiency with automatic scoring and feedback

Selection an option below to learn more.

How the e-rater engine uses AI technology

ETS is a global leader in educational assessment, measurement and learning science. Our AI technology, such as the e-rater ® scoring engine, informs decisions and creates opportunities for learners around the world.

The e-rater engine automatically:

• assess and nurtures key writing skills
• scores essays and provides feedback on writing using a model built on the theory of writing to assess both analytical and independent writing skills

About the e-rater Engine

This ETS capability identifies features related to writing proficiency.

How It Works

See how the e-rater engine provides scoring and writing feedback.

Custom Applications

Use standard prompts or develop your own custom model with ETS’s expertise.

Use in Criterion ® Service

Learn how the e-rater engine is used in the Criterion ® Service.

FEATURED RESEARCH

E-rater as a Quality Control on Human Scores

See All Research (PDF)

Ready to begin? Contact us to learn how the e-rater service can enhance your existing program.

Welcome to EssayGrader – where innovation meets education! 📚

In the realm of education, where the demands on teachers seem endless, EssayGrader emerged as a beacon of relief, born from a singular vision: lightening the grading burden for teachers. Picture this: educators faced with a daunting 200-to-1 student-to-teacher ratio for a single writing assignment. It's a challenge we understand all too well. With the power of artificial intelligence at our fingertips, we crafted a solution that not only eases this load but transforms the entire grading experience.

At the heart of our journey are two passionate individuals: Payton, a visionary software engineer, and Ashley, a dedicated English teacher. Together, they embarked on the mission to revolutionize how teachers approach grading. The result? EssayGrader, a groundbreaking product meticulously designed to save time and energy. It's astonishing: what used to take an average of 10 minutes per essay can now be expertly handled in just 30 seconds, marking a phenomenal 95% reduction in grading time.

EssayGrader is more than just a tool; it's a testament to our unwavering commitment to educators. We've witnessed the exhaustion that plagues classrooms, and we set out to create something impactful. Through relentless dedication and numerous iterations, we've developed a product that resonates deeply with teachers – a tool they love and trust.

But our journey doesn't end here. EssayGrader is a dynamic creation in constant evolution. We are not merely satisfied; we are driven to refine, improve, and adapt. How? By engaging with our users, the lifeblood of our community. Your insights fuel our innovation. We are steadfast in our promise to listen, learn, and implement changes that empower both educators and students. Together, we're not just envisioning the future of education; we're shaping it.

Join us on this transformative expedition. Be a part of the EssayGrader family, where every click signifies progress, every grade transforms a student's journey, and every educator finds the support they deserve. Together, let's redefine education. Together, let's make a difference.

Meet the team

Payton Burdette is the Founder of EssayGrader and writes all the code behind the product. He has over 10+ years of coding experience and loves what he does. If there are any new features added or bugs fixed, he is the guy that's making those changes.

In his free time you can catch him playing video games, working out or spending time with his family. He also loves to go fishing and watch Clemson football games.

Ashley Burdette is the Product Manager of EssayGrader. She helps Payton out by mapping a path forward for EssayGrader to make it a product loved by all teachers. Given her experience teaching for over 10+ years, she brings a lot to the table and knows what our users need to make their lives easier.

In her free time she loves watching TV, spending time with her family and going out with friends. She also loves vacations, especially going to the beach and hunting for shark teeth.

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Essay Grader

An essay grader or a paper grader is an easier way for students to evaluate how well-written their papers are before turning them in or to shorten grading time for teachers. Most of these exist in online essay grader format where you input your specific assignment requirements and measure whether or not the student fulfilled them or not.

Others exist in an essay rater format where a student’s essay is evaluated by their language use, transitioning, spelling, and grammar. If you’ve been looking for something to “grade my essay,” here are a few online sources that you may find particularly valuable. Earlier, students simply had to wait to get their papers returned to them to know if they were any good or not. But today, many students have simply searched, “check my essay” and have seen how their papers could be improved before final submission.

One such free online source is PaperRater.com . With PaperRater, students or teachers can perform grammar and spelling checks, get free proofreading, check for plagiarism, check for style and word analysis, see what the essay’s readability statistics are, see if the title is relevant, and even build up their vocabulary with their online vocabulary building tool. While these are all wonderful free online tools, there are a number of drawbacks that users have found with PaperRater. It takes a long time for papers to load and its accuracy is not the best. Some users found that simple mistakes, like spelling, capitalization, and other punctuation errors were missed. Therefore, while it may not be the best online paper grader, it will give you a brief analysis of where you stand with your paper.

Paper Rater

A great source for teachers is EssayTagger.com . This site helps teachers to grade essays faster by cutting down on time they spend writing the same commentary over and over. It is not an auto grader because it only cuts down on repetitive teacher’s remarks. To use, simply enter in the necessary rubric in the wizard. You must tag the student’s “thesis” or central idea and then customize all the other necessary elements. This program automatically alerts teachers if something is awkwardly worded and can indicate where in the text the student struggles. EssayTagger is a free online source and has been met with mostly positive reviews.

If you prefer an app to do all the same functions as the above-listed programs then Apple’s Essay Grader app is for you. While it’s not free, it is available at the reasonable price of $6.99. This app allows you to avoid repetitive commentary, customize any commentary, and save it in the app. You can also customize column labels, discipline, rubric, and grading style. Most users were generally pleased with the product, though they found the given commentary from the program to be over-the-top at times.

For students seeking to improve their writing and for teachers and school administrators wanting to monitor their students’ progress, the Glencoe Online Essay Grader is a great tool. As a teacher, you can provide assignment instructions online with reporting tools designed to improve student performance. It also has individualized automatic scoring so you can adjust it to suit your specific needs. Additionally, grade-level appropriate prompts can be assigned from the Glencoe Literature series. It also has great writing tools for students to help them improve their writing skills by analyzing them with quantitative scores. Additionally, teachers and school administrators can monitor how students’ writing skills are improving within a particular class, school, or even district. The price for the Glencoe Online Essay Grader could not be found, though it is doubtful that this is a free product given all its capabilities.

While all the above-mentioned products are designed with students and teachers in mind, the GMAT Essay Rater at GMATAWA.com is there to assist future business school students to improve their GMAT test scores. The GMAT or Graduate Management Admission Test is used to assess a person’s skills in analytical writing, quantitative sciences, reading, and verbal areas. This works by the person choosing an argument and then requesting an attempt code. Once you have the attempt code, enter in or copy paste your essay, attempt code, and then receive your results. Since there is an attempt code, there is likely a set number of attempts that people are allowed.

GMATAWA.com

These are all wonderful tools for improving your writing skills as a student or to shorten essay grading time if you are a teacher. Other great educational tools include QuickGPA.com that allows you to calculate GPA online. If you are searching for a scientific calculator online , there is no better choice than the Desmos Graphing Calculator that can also be used as a Google Chrome app.

With so many wonderful educational programs and apps online, you simply can’t lose!

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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

Students write their essays or other ECR responses

Users mark responses

Users (e.g., teachers) log in and mark student responses on your specified rubrics, as well as flag responses or leave comments. Admins can adjudicate any disagreements.

Score examinees

Examinees will be automatically scored.  For example, if your test has 40 multiple choice items and an essay with two 5-point rubrics, the total score is 50.  We also support the generalized partial credit model from item response theory, or exporting results to analyze in other software like FACETS.

Sign up for a SmartMarq account

Simply upload your student essays and human marking results, and our AI essay scoring system will provide an additional set of marks.

Need a complete platform to manage the entire assessment cycle, from item banking to online delivery to scoring?  FastTest provides the ideal solution.  It includes an integrated version of SmartMarq with advanced options like scoring rubrics with the Generalized Partial Credit Model .

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Essay Grader

A smarter way to grade essays with Essay Grader.

EssayGrader.ai is a cutting-edge AI grading tool designed to streamline the essay grading process for teachers. This powerful tool offers a range of features, including feedback reports based on rubrics, detailed error reports that highlight grammar, spelling, and punctuation mistakes, and a summarizer feature that quickly generates concise summaries of essays. With EssayGrader.ai, teachers can save time and focus on providing meaningful feedback to their students. Additionally, the tool is constantly evolving, with an AI detector feature in development that will enable teachers to identify if an essay was written by AI or if only parts of it were written by AI. Overall, EssayGrader.ai is an invaluable productivity tool for teachers looking to streamline their grading process while still providing comprehensive feedback to their students.

Check out more tools like Essay Grader

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Essay-Grading Software Seen as Time-Saving Tool

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Jeff Pence knows the best way for his 7th grade English students to improve their writing is to do more of it. But with 140 students, it would take him at least two weeks to grade a batch of their essays.

So the Canton, Ga., middle school teacher uses an online, automated essay-scoring program that allows students to get feedback on their writing before handing in their work.

“It doesn’t tell them what to do, but it points out where issues may exist,” said Mr. Pence, who says the a Pearson WriteToLearn program engages the students almost like a game.

With the technology, he has been able to assign an essay a week and individualize instruction efficiently. “I feel it’s pretty accurate,” Mr. Pence said. “Is it perfect? No. But when I reach that 67th essay, I’m not real accurate, either. As a team, we are pretty good.”

With the push for students to become better writers and meet the new Common Core State Standards, teachers are eager for new tools to help out. Pearson, which is based in London and New York City, is one of several companies upgrading its technology in this space, also known as artificial intelligence, AI, or machine-reading. New assessments to test deeper learning and move beyond multiple-choice answers are also fueling the demand for software to help automate the scoring of open-ended questions.

Critics contend the software doesn’t do much more than count words and therefore can’t replace human readers , so researchers are working hard to improve the software algorithms and counter the naysayers.

While the technology has been developed primarily by companies in proprietary settings, there has been a new focus on improving it through open-source platforms. New players in the market, such as the startup venture LightSide and edX , the nonprofit enterprise started by Harvard University and the Massachusetts Institute of Technology, are openly sharing their research. Last year, the William and Flora Hewlett Foundation sponsored an open-source competition to spur innovation in automated writing assessments that attracted commercial vendors and teams of scientists from around the world. (The Hewlett Foundation supports coverage of “deeper learning” issues in Education Week .)

“We are seeing a lot of collaboration among competitors and individuals,” said Michelle Barrett, the director of research systems and analysis for CTB/McGraw-Hill, which produces the Writing Roadmap for use in grades 3-12. “This unprecedented collaboration is encouraging a lot of discussion and transparency.”

Mark D. Shermis, an education professor at the University of Akron, in Ohio, who supervised the Hewlett contest, said the meeting of top public and commercial researchers, along with input from a variety of fields, could help boost performance of the technology. The recommendation from the Hewlett trials is that the automated software be used as a “second reader” to monitor the human readers’ performance or provide additional information about writing, Mr. Shermis said.

“The technology can’t do everything, and nobody is claiming it can,” he said. “But it is a technology that has a promising future.”

‘Hot Topic’

The first automated essay-scoring systems go back to the early 1970s, but there wasn’t much progress made until the 1990s with the advent of the Internet and the ability to store data on hard-disk drives, Mr. Shermis said. More recently, improvements have been made in the technology’s ability to evaluate language, grammar, mechanics, and style; detect plagiarism; and provide quantitative and qualitative feedback.

The computer programs assign grades to writing samples, sometimes on a scale of 1 to 6, in a variety of areas, from word choice to organization. The products give feedback to help students improve their writing. Others can grade short answers for content. To save time and money, the technology can be used in various ways on formative exercises or summative tests.

The Educational Testing Service first used its e-rater automated-scoring engine for a high-stakes exam in 1999 for the Graduate Management Admission Test, or GMAT, according to David Williamson, a senior research director for assessment innovation for the Princeton, N.J.-based company. It also uses the technology in its Criterion Online Writing Evaluation Service for grades 4-12.

Over the years, the capabilities changed substantially, evolving from simple rule-based coding to more sophisticated software systems. And statistical techniques from computational linguists, natural language processing, and machine learning have helped develop better ways of identifying certain patterns in writing.

But challenges remain in coming up with a universal definition of good writing, and in training a computer to understand nuances such as “voice.”

In time, with larger sets of data, more experts can identify nuanced aspects of writing and improve the technology, said Mr. Williamson, who is encouraged by the new era of openness about the research.

“It’s a hot topic,” he said. “There are a lot of researchers and academia and industry looking into this, and that’s a good thing.”

High-Stakes Testing

In addition to using the technology to improve writing in the classroom, West Virginia employs automated software for its statewide annual reading language arts assessments for grades 3-11. The state has worked with CTB/McGraw-Hill to customize its product and train the engine, using thousands of papers it has collected, to score the students’ writing based on a specific prompt.

“We are confident the scoring is very accurate,” said Sandra Foster, the lead coordinator of assessment and accountability in the West Virginia education office, who acknowledged facing skepticism initially from teachers. But many were won over, she said, after a comparability study showed that the accuracy of a trained teacher and the scoring engine performed better than two trained teachers. Training involved a few hours in how to assess the writing rubric. Plus, writing scores have gone up since implementing the technology.

Automated essay scoring is also used on the ACT Compass exams for community college placement, the new Pearson General Educational Development tests for a high school equivalency diploma, and other summative tests. But it has not yet been embraced by the College Board for the SAT or the rival ACT college-entrance exams.

The two consortia delivering the new assessments under the Common Core State Standards are reviewing machine-grading but have not committed to it.

Jeffrey Nellhaus, the director of policy, research, and design for the Partnership for Assessment of Readiness for College and Careers, or PARCC, wants to know if the technology will be a good fit with its assessment, and the consortium will be conducting a study based on writing from its first field test to see how the scoring engine performs.

Likewise, Tony Alpert, the chief operating officer for the Smarter Balanced Assessment Consortium, said his consortium will evaluate the technology carefully.

Open-Source Options

With his new company LightSide, in Pittsburgh, owner Elijah Mayfield said his data-driven approach to automated writing assessment sets itself apart from other products on the market.

“What we are trying to do is build a system that instead of correcting errors, finds the strongest and weakest sections of the writing and where to improve,” he said. “It is acting more as a revisionist than a textbook.”

The new software, which is available on an open-source platform, is being piloted this spring in districts in Pennsylvania and New York.

In higher education, edX has just introduced automated software to grade open-response questions for use by teachers and professors through its free online courses. “One of the challenges in the past was that the code and algorithms were not public. They were seen as black magic,” said company President Anant Argawal, noting the technology is in an experimental stage. “With edX, we put the code into open source where you can see how it is done to help us improve it.”

Still, critics of essay-grading software, such as Les Perelman, want academic researchers to have broader access to vendors’ products to evaluate their merit. Now retired, the former director of the MIT Writing Across the Curriculum program has studied some of the devices and was able to get a high score from one with an essay of gibberish.

“My main concern is that it doesn’t work,” he said. While the technology has some limited use with grading short answers for content, it relies too much on counting words and reading an essay requires a deeper level of analysis best done by a human, contended Mr. Perelman.

“The real danger of this is that it can really dumb down education,” he said. “It will make teachers teach students to write long, meaningless sentences and not care that much about actual content.”

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An automated essay scoring systems: a systematic literature review

Dadi ramesh.

1 School of Computer Science and Artificial Intelligence, SR University, Warangal, TS India

2 Research Scholar, JNTU, Hyderabad, India

Suresh Kumar Sanampudi

3 Department of Information Technology, JNTUH College of Engineering, Nachupally, Kondagattu, Jagtial, TS India

Associated Data

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-021-10068-2.

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 .

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.

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.

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.

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.

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 ​ Table1. 1 . After Quality Assessment, the final list of papers for review is shown in Table ​ Table2. 2 . The complete selection process is shown in Fig. ​ Fig.1. 1 . The total number of selected papers in year wise as shown in Fig. ​ Fig.2. 2 .

Quality assessment analysis

Final list of papers

Selection process

Year wise publications

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 ​ Table3 3 illustrates all datasets related to AES systems.

ALL types Datasets used in Automatic scoring systems

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 ​ Table4 4 represents all set of features used for essay grading.

Types of features

We studied all the feature extracting NLP libraries as shown in Fig. ​ Fig.3. that 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. ​ Fig.4 4 as observed that non-content-based feature extraction is higher than content-based.

Usages of tools

Number of papers on content based features

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.

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.

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.

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.

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.

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.

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.

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 ​ Table5 5 with a comparative study of the AES systems.

State of the art

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 ​ Table6. 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.

Vector representation of essays

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 ​ Table7 7 represents a comparison of Machine Learning models and features extracting methods.

Comparison of models

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 ​ Table8 8 represents all four parameters comparison for essay grading. Table ​ Table9 9 illustrates comparison of all approaches based on various features like grammar, spelling, organization of essay, relevance.

Comparison of all models with respect to cohesion, coherence, completeness, feedback

comparison of all approaches on various features

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.

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.

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 ​ Table3 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 ​ Table3. 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. ​ Fig.3. 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.

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.

Below is the link to the electronic supplementary material.

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Publisher's Note

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Contributor Information

Dadi Ramesh, Email: moc.liamg@44hsemaridad .

Suresh Kumar Sanampudi, Email: ni.ca.hutnj@idupmanashserus .

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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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When You Write

Best Essay Writing Software: 16 Apps That Can Help You Write Perfect Essays

Nowadays, we have apps for almost anything. Writing apps though, are really unique and serve several practical purposes, such as essay writing for example.

Writing an essay isn’t as simple as typing a bunch of words and arranging them as paragraphs. Writing a perfect essay entails planning, maintaining structure, writing clear and impactful sentences, and using good grammar.

That’s why we have writing apps. So in this post, I’m going to cover all the best essay writing software tools that are available now.

Okay, let’s get into it!

Our Top 3 Essay Writing Software at a Glance

Prowritingaid, the 16 best essay writing apps.

Best Book Writing Software.

Scrivener is a book-writing software program developed—13 years ago—by an aspiring writer Keith Blount.

Scrivener is what you get when you combine a typewriter, ring binder, and a scrapbook and make a book writing software tool.

It has so many useful features that take your book or any other writing project from the outline to a finished draft. We’re talking of features like corkboards, pre-set formatting, templates, file importing, metatags, automated document listing, and a bunch of other important capabilities.

Scrivener can be used by novelists, scriptwriters, academics, lawyers, translators, journalists, and students.

Pricing: Scrivener for macOS costs $49, iOS is $19.99 , and Scrivener for Windows costs $45.

Best Grammar Checker.

As far as grammar checkers go, there’s no app better than this.

It’s perfect for checking your text for typos, punctuation, and spelling mistakes. 

But it goes further than that; it has several editing features that tackle issues such as passive voice, lengthy or complex sentences, offers broader vocabulary options, et cetera. It also has a plagiarism checker and furnishes you with writing stats and readability scores.

Grammarly has a free version but reserves the best features for its premium plans.

Pricing: Free and Paid. Grammarly Premium starts at $11.66, and Grammarly Business starts at $12.50.

Excellent Tool for Self-editing

ProWritingAid is one of Grammarly’s fiercest competitors and pretty much gives you what Grammarly gives you.

Just itty-bitty shallower, BUT way more affordable.

If you want a proofreading and grammar checker writing app that has the potential of helping you improve the overall quality of your writing, this is it!

ProWritingAid refines your writing by checking important elements of your writing such as ambiguous sentences, grammar, transitions, abstract words, overused words, etc.

Over time, you start to notice improvements in your writing, especially the way it flows.

Pricing: Free and Paid.

• Monthly – $20
• Yearly – $79
• Lifetime – $399 

Best note-taking tool for Students

Evernote is a simple but excellent writing app that uses the idea of virtual notes. The virtual notes can be used in several ways: making book shopping lists, writing down essay ideas, and making notes on researched information.

The notes are backed up on Evernote’s servers, and you get about 60MB of storage space per account.

Pricing :  Free and Paid. The Premium plan costs $7.99 / month, and the Business costs $14.99/person/month.

Focus Writer

Free Alternative to MS Word.

I haven’t used this free word-processing app yet, but I’d seriously consider replacing my beloved MS Word with it.

It’s a very good alternative to MS Word; in fact, not only is it free and sufficient, it is available on almost all major platforms— Windows, Mac, and Android.

If you’re a student, I’d recommend this software, and you won’t have problems using the documents from this app because they’re saved in Rich Text Format so that most word processors can read them.

Pricing: Free.

Excellent for the Creative Stage of Writing

Before you write a good essay, you need to build the idea behind the essay first.

You need to add fresh to the bones before bringing the beast (of an essay) to life.

This tool helps you expand your original ideas into sub-ideas and construct full-fledged essays by using expressive, powerful flowcharts, process maps, and other diagrams.

Pricing: Free and Paid. The Awesome Plan costs $5/month, and the Organisation Plan is at $8/member/month.

Top-Notch Open-Source Tool

Manuskript is a tool perfect for organizing and planning stages of writing. It’s an open-source tool—for writers including novelists, journos, and academicians—that uses the snowflake method of writing to help you build your idea into a finished book; by helping you create the story step by step.

It has features for helping you keep track of notes on characters, plot, event,  and place in your story. Manuskript’s features include:

• The Outliner, which lets you organize your ideas and little pieces of your story hierarchically; 
• The Distraction-free mode gets rid of all distractions; 
• Personal goal-setting features;
• The Novel assistant utilizes the snowflake method to help you develop your basic ideas into a coherent plotline or a full-fledged story.

Pricing: Free

LivingWriter

Fast Developing Tool

This is a different writing app altogether. It has a lot of features that are excellent for both fiction nonfiction writers. It has features that help with story elements, general notes, goals and targets, doc sharing, and stats.

Plus, you can also switch LivingWriter in and out of Dark Mode and focus mode.

One thing I like about LivingWriter is that it started with a single platform (web) but is now expanding rapidly.

It has iOS and Android applications, Full integration with Grammarly, Canva integration, and its desktop apps are 90% complete (according to the Living Writer Roadmap )

Yearly Plan – $96 billed once a year

Monthly Plan – $9.99 per month

Best Writing Tool for Apple Products

I don’t know about now, but Ulysses was big back then (like a dozen years ago or so).

The app is rich with features similar to the other word processing software. It comes with a Markup-Based Text Editor, a library for organizing notes and documents, features for setting writing goals, publishing capabilities, and many others.

Ulysses is perfect for both small essays and large academic ones.

Pricing : Ulysses has different pricing options for different regions but using the US plan, it costs $5.99 per month and $49.99 per year.

Hemingway Editor

An App Most Impactful Writing

The Hemingway Editor AKA Hemingway App is a simple tool for writers who want to write content that is easier to read but bold.

Hemingway does this by looking at elements of your writing such as adverbs, passive voice, phrases and words with simpler alternatives, hard-to-read sentences, very-hard-to-read sentences, and other “lexical atrocities.”

The web-based version is free, but the downloadable version (for Mac and Windows) is a paid tool.

Pricing: $19.99

Excellent Mind Mapping Tool

They used to call this app IMindMap. Ayoa is an essential tool in the planning stages of your essay writing.

You can create mind maps for your essays which help give direction when you start fleshing out your essay.

This mapping tool helps increase productivity because everything you need to write is already outlined. The fact every step is already planned and you know exactly what to write can also increase your daily word count.

Pricing : The Ayoa PRO plan costs $10/month and is billed annually. The Ultimate Plan costs $13/month and is also billed annually.

Best App for Multi-Lingual Essays

This is another incredible alternative to Grammarly.

For non-native English speakers, this is a pot of gold right here. It can check your text for grammar errors and translate Spanish, French, German. And many other languages.

As a grammar checker, the tool mainly looks at aspects such as verbs, adverbs, confused words, commonly misspelled words, etc.

Here’s a funny story about Ginger (Just happened today.) As I was using the web-based editor, I copied some texts on Ginger’s website and pasted them into the editor and the tool found one misspelled word. Their content writers must not have used Ginger.

I felt like Ginger’s content writers were like Drug dealers, you know, they followed rule number one of drug dealing. “ never get high on your own product.”   

• Monthly Plan – $9.99
• Yearly Plan – $74.88
• Two-Year Plan: $119.76

Write Or Die

Best for Productivity Purposes.

This app has a name that sums up life for some of us.

Write or Die!

Write Or Die gives rewards, stimuli, and punishments if users set goals. This is what you need if you are a sloth like myself.

It gives you that needed push because the punishments—which include erasing current texts—can really scare the hell out of you and make you put an extra gear.

Pricing : Free and paid. $10 for macOS and Windows, and $1 for iOS.

Simplest Writing App

IA Writer is a distraction-free writing app that is more than perfect for writing short essays.

It has a very basic interface and uses plain text. As I said, there are no distractions because the IA writer also has a full-screen mode that fades out everything else but the line you are currently typing.

This minimalist writing app is available on Windows, Android, iOS, iPadOS, and macOS.

Pricing : Paid (offers free trials).

macOS- $29.99. has a 14-day trial

iOS &iPadOS – $29.99. No Free Trial.

Android – $4.99 /year or $29.99 once. Has a 30-day trial.

Windows – $29.99. Has a 14-day trial.

Hubspot Topic Generator

Best for Generating Topic Ideas.

This is a whole different menu right here.

It’s very different from the tools that I’ve listed in this post. This is why… you’re not going to use it to write. Instead, you use it to automatically generate writing ideas.

It’s a very simple tool; you input three words, and it provides you with nouns to generate a topic idea that you can use on your next writing project.

If you’re going to use this tool, it’s going to be during the very first stages of your writing project.

Manuscripts

Best App for Academic Assignments

Manuscript (not to be confused with Manuskript from above) is an app for students and academics. This is a convenient tool that works with popular word processing apps, including Microsoft Word.

The reason why it’s perfect for academic writing is it excels at the referencing aspect of writing—citations, abbreviations, etc.

So, for class writing assignments and larger tasks like dissertations, this is the tool I’d recommend.

Pricing : Free

Simplenote is a note-taking tool that helps you keep all your notes in one place but accessible everywhere.

You can back up your notes, add tags, share the notes with collaborators, and publish your notes in Markdown format.

Supported Systems: Android, iOS, Windows, macOS, Linux.

Guide to the Best Essay Writing Apps in 2021

Things to consider when choosing essay writing software.

A good essay writing software has to do things that make your essay look delectable and sound convincing.

Here are some of the things that an essay writing software need to be able to help you with:

Organization

I already talked about organization at the beginning, so you already know how important it is. The essay writing app has to help you arrange your essay and ensure that it flows nicely. It needs to clear the chaos that would have existed had you not used that specific tool.

Grammatical correctness

This is crucial in any writing project. The essay writing software has to help you correct your grammatical and spelling errors.

Proofreading

The proofreading capabilities of a good essay writing app go beyond checking for grammar and spelling mistakes. It also has to excel at checking other aspects like overuse of adverbs, passive voice, run-on sentences, weak writing , and readability.

Writing software doesn’t have to cost an arm. Most of these apps have similar features and pretty much do the same things. A higher price doesn’t necessarily mean the app is good but in some circumstances, apps are pricey for a good reason.

What Features Should an Essay Writing App Have

So, to achieve the above requirements, what features does a writing software need to have?

Below are the most important features that a good essay writing software program MUST have.

Grammar and Spelling checker

To achieve the overall grammatical correctness of your essay, a writing app needs to have a grammar checking feature. If it doesn’t, being integrable with an efficient grammar checking tool is also convenient.

Sentence structure and flow reports

Again, I cannot overemphasize the importance of structure in essay writing. The structure should start from your sentences to your paragraphs and the whole essay. A good essay writing app needs to have features that check aspects of structure and flow.

Punctuation checker

We can’t have an essay littered with semicolons, commas, and hyphens looking like they’re lost. Punctuation might be one of the most underrated features but make no mistake, it’s essential for professional writing.

Plagiarism checker

A good essay has to be unique and original. Therefore, essay writing software has to make sure that the body of the essay does not contain any plagiarized content.

Writing Metrics

Stats like word count, words per minute, or the number of pages are important for tracking progress. School essays usually have a word or page count requirements, and writing software must be equipped with writing metrics so that the user is kept abreast of the distance covered.

Sentence quality checker

In the writing profession, Quality matters.  It doesn’t matter if you have reached the minimum word count but the essay is of poor quality.

Writing apps must be able to pick out sentences that need improving or deleting due to poor quality writing.

Why Should You Use Writing Software to Write Essays?

Writing software won’t write your essay for you, but the writing process is hard to manage and that’s what these tools do.

Here are the benefits of using writing software:

1. Planning and Outlining

Planning is an important element of a good essay writing process. Writing software tools come with features that help you plan before you start writing.

For example, Scrivener has a feature called corkboard, which is a good planning tool. It’s like a set of digital index cards, and each represents a section of writing.

With writing software, you can plan and outline before the actual writing starts, and you can go back to the outlines and notes while writing.

2. Productivity

Productivity is a big problem for most writers. Writers like myself just write without setting a lot of writing goals, so when we feel like writing, we need to be at our most productive levels.

For those that set daily goals, maintaining a daily word count is not easy.

For both kinds of writers, writing software can help increase productivity.

The software tools come with writing stats to help you keep track of your progress. They also have features for distraction-free writing.

Templates also help increase productivity. The templates make things easier and save you a lot of time (which would have been used setting things up).

3. Editing and Formatting

Writing software tools come with features that can flag spelling & grammar mistakes and other errors. They also offer solutions to these errors.

This is very important for your editing process—it makes the editing stage easier and faster.

This also helps in increasing productivity since editing is less laborious and speedy.

Usually when we write essays (especially academic ones), some formatting requirements come with them. Writing apps are furnished with most of the formatting rules and styles that essays (academic or otherwise) may require.

4. Organizing

Writing can be a messy process.

Most often than not, essays also require a lot of research. And again, we’re not saying that writing software will help you research.

But when you get all the bits of info needed for your essay, the apps will help you keep the researched content organized.

With these writing apps, you can have all of your research organized and easily accessible.

The thing about a disorganized writing process is that it is reflected in the flow and structure of the essay.

How to Effectively Use Essay Writing Software

Let me reiterate, essay writing apps won’t write your essays for you, neither will they be correct all the time.

To get the best out of them, you need to treat them as writing tutors or co-writers. If they suggest something useful, take it on board, and if you feel like the suggestion is a bit off point, disregard it.

What Is a Perfect Essay?

A perfect essay convincingly speaks to the reader. An essay is like an argument or a speech, and it has to have a readable flow or show direction.

Perfect essays must contain arguments, supporting ideas, and most importantly, evidence.  

To write a perfect essay, you need to:

• Thoroughly plan the whole essay before you start writing.
• Start writing your arguments using a clear structure.
• Back up your points and refer to relevant sources if necessary.
• Make sure that you infuse the information with creativity. There’s nothing exciting about a bunch of truths thrown into an essay using bland sentences.
• Before you finish your draft, ensure that you’ve answered the question in your introduction and conclusion.

How can I write an essay on my phone?

Well, most of the apps listed here are available as mobile apps. If you feel it’d be okay to write on your phone, try out a couple of the apps on this list and see which one works better on mobile platforms.

I’d recommend using tablet computers as they have bigger screens than regular smartphones.

Final Words

There are just so many essay writing software tools nowadays that even though Scrivener and Grammarly top the list, stumbling on the best one for you is almost 1/1000 probable.

You have to try out these tools before purchasing them.

Just to say it for the one-thousandth time, essay writing apps won’t write your essays for you; YOU WILL.

Recommended Reading...

Best dictation software in 2024, scrivener vs word: which is the better book writing software, vellum vs scrivener: which is better for writing and formatting your book, write app review 2024: the best distraction-free writing app.

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Teachers are using AI to grade essays. But some experts are raising ethical concerns

W hen Diane Gayeski, a professor of strategic communications at Ithaca College, receives an essay from one of her students, she runs part of it through ChatGPT, asking the AI tool to critique and suggest how to improve the work.

“The best way to look at AI for grading is as a teaching assistant or research assistant who might do a first pass … and it does a pretty good job at that,” she told CNN.

She shows her students the feedback from ChatGPT and how the tool rewrote their essay. “I’ll share what I think about their intro, too, and we’ll talk about it,” she said.

Gayeski requires her class of 15 students to do the same: run their draft through ChatGPT to see where they can make improvements.

The emergence of AI is reshaping education, presenting real benefits, such as automating some tasks to free up time for more personalized instruction, but also some big hazards, from issues around accuracy and plagiarism to maintaining integrity.

Both teachers and students are using the new technology. A report by strategy consultant firm Tyton Partners, sponsored by plagiarism detection platform Turnitin, found half of college students used AI tools in Fall 2023. Meanwhile, while fewer faculty members used AI, the percentage grew to 22% of faculty members in the fall of 2023, up from 9% in spring 2023.

Teachers are turning to AI tools and platforms — such as ChatGPT, Writable, Grammarly and EssayGrader — to assist with grading papers, writing feedback, developing lesson plans and creating assignments. They’re also using the burgeoning tools to create quizzes, polls, videos and interactives to up the ante” for what’s expected in the classroom.

Students, on the other hand, are leaning on tools such as ChatGPT and Microsoft CoPilot — which is built into Word, PowerPoint and other products.

But while some schools have formed policies on how students can or can’t use AI for schoolwork, many do not have guidelines for teachers. The practice of using AI for writing feedback or grading assignments also raises ethical considerations. And parents and students who are already spending hundreds of thousands of dollars on tuition may wonder if an endless feedback loop of AI-generated and AI-graded content in college is worth the time and money.

“If teachers use it solely to grade, and the students are using it solely to produce a final product, it’s not going to work,” said Gayeski.

The time and place for AI

How teachers use AI depends on many factors, particularly when it comes to grading, according to Dorothy Leidner, a professor of business ethics at the University of Virginia. If the material being tested in a large class is largely declarative knowledge — so there is a clear right and wrong — then a teacher grading using the AI “might be even superior to human grading,” she told CNN.

AI would allow teachers to grade papers faster and more consistently and avoid fatigue or boredoms, she said.

But Leidner noted when it comes to smaller classes or assignments with less definitive answers, grading should remain personalized so teachers can provide more specific feedback and get to know a student’s work, and, therefore, progress over time.

“A teacher should be responsible for grading but can give some responsibility to the AI,” she said.

She suggested teachers use AI to look at certain metrics — such as structure, language use and grammar — and give a numerical score on those figures. But teachers should then grade students’ work themselves when looking for novelty, creativity and depth of insight.

Leslie Layne, who has been teaching ChatGPT best practices in her writing workshop at the University of Lynchburg in Virginia, said she sees the advantages for teachers but also sees drawbacks.

“Using feedback that is not truly from me seems like it is shortchanging that relationship a little,” she said.

She also sees uploading a student’s work to ChatGPT as a “huge ethical consideration” and potentially a breach of their intellectual property. AI tools like ChatGPT use such entries to train their algorithms on everything from patterns of speech to how to make sentences to facts and figures.

Ethics professor Leidner agreed, saying this should particularly be avoided for doctoral dissertations and master’s theses because the student might hope to publish the work.

“It would not be right to upload the material into the AI without making the students aware of this in advance,” she said. “And maybe students should need to provide consent.”

Some teachers are leaning on software called Writable that uses ChatGPT to help grade papers but is “tokenized,” so essays do not include any personal information, and it’s not shared directly with the system.

Teachers upload essays to the platform, which was recently acquired by education company Houghton Mifflin Harcourt, which then provides suggested feedback for students.

Other educators are using platforms such as  Turnitin  that boast plagiarism detection tools to help teachers identify when assignments are written by ChatGPT and other AI. But these types of detection tools are far from foolproof; OpenAI shut down its own AI-detection tool last year due to what the company called a “low rate of accuracy.”

Setting standards

Some schools are actively working on policies for both teachers and students. Alan Reid, a research associate in the Center for Research and Reform in Education (CRRE) at Johns Hopkins University, said he recently spent time working with K-12 educators who use GPT tools to create end-of-quarter personalized comments on report cards.

But like Layne, he acknowledged the technology’s ability to write insightful feedback remains “limited.”

He currently sits on a committee at his college that’s authoring an AI policy for faculty and staff; discussions are ongoing, not just for how teachers use AI in the classroom but how it’s used by educators in general.

He acknowledges schools are having conversations about using generative AI tools to create things like promotion and tenure files, performance reviews, and job postings.”

Nicolas Frank, an associate professor of philosophy at University of Lynchburg, said universities and professors need to be on the same page when it comes to policies but need to stay cautious .

“There is a lot of danger in making policies about AI at this stage,” he said.

He worries it’s still too early to understand how AI will be integrated into everyday life. He is also concerned that some administrators who don’t teach in classrooms may craft policy that misses nuances of instruction.

“That may create a danger of oversimplifying the problems with AI use in grading and instruction,” he said. “Oversimplification is how bad policy is made.”

To start, he said educators can identify clear abuses of AI and begin policy-making around those.

Leidner, meanwhile, said universities can be very high level with their guidance, such as making transparency a priority — so students have a right to know when AI is being used to grade their work — and identifying what types of information should never be uploaded into an AI or asked of an AI.

But she said universities must also be open to “regularly reevaluating as the technology and uses evolve.”

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