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College Essays

If you grow up to be a professional writer, everything you write will first go through an editor before being published. This is because the process of writing is really a process of re-writing —of rethinking and reexamining your work, usually with the help of someone else. So what does this mean for your student writing? And in particular, what does it mean for very important, but nonprofessional writing like your college essay? Should you ask your parents to look at your essay? Pay for an essay service?

If you are wondering what kind of help you can, and should, get with your personal statement, you've come to the right place! In this article, I'll talk about what kind of writing help is useful, ethical, and even expected for your college admission essay . I'll also point out who would make a good editor, what the differences between editing and proofreading are, what to expect from a good editor, and how to spot and stay away from a bad one.

Table of Contents

What Kind of Help for Your Essay Can You Get?

What's Good Editing?

What should an editor do for you, what kind of editing should you avoid, proofreading, what's good proofreading, what kind of proofreading should you avoid.

What Do Colleges Think Of You Getting Help With Your Essay?

Who Can/Should Help You?

Advice for editors.

Should You Pay Money For Essay Editing?

The Bottom Line

What's next, what kind of help with your essay can you get.

Rather than talking in general terms about "help," let's first clarify the two different ways that someone else can improve your writing . There is editing, which is the more intensive kind of assistance that you can use throughout the whole process. And then there's proofreading, which is the last step of really polishing your final product.

Let me go into some more detail about editing and proofreading, and then explain how good editors and proofreaders can help you."

Editing is helping the author (in this case, you) go from a rough draft to a finished work . Editing is the process of asking questions about what you're saying, how you're saying it, and how you're organizing your ideas. But not all editing is good editing . In fact, it's very easy for an editor to cross the line from supportive to overbearing and over-involved.

Ability to clarify assignments. A good editor is usually a good writer, and certainly has to be a good reader. For example, in this case, a good editor should make sure you understand the actual essay prompt you're supposed to be answering.

Open-endedness. Good editing is all about asking questions about your ideas and work, but without providing answers. It's about letting you stick to your story and message, and doesn't alter your point of view.

Think of an editor as a great travel guide. It can show you the many different places your trip could take you. It should explain any parts of the trip that could derail your trip or confuse the traveler. But it never dictates your path, never forces you to go somewhere you don't want to go, and never ignores your interests so that the trip no longer seems like it's your own. So what should good editors do?

Help Brainstorm Topics

Sometimes it's easier to bounce thoughts off of someone else. This doesn't mean that your editor gets to come up with ideas, but they can certainly respond to the various topic options you've come up with. This way, you're less likely to write about the most boring of your ideas, or to write about something that isn't actually important to you.

If you're wondering how to come up with options for your editor to consider, check out our guide to brainstorming topics for your college essay .

Help Revise Your Drafts

Here, your editor can't upset the delicate balance of not intervening too much or too little. It's tricky, but a great way to think about it is to remember: editing is about asking questions, not giving answers .

Revision questions should point out:

• Places where more detail or more description would help the reader connect with your essay
• Places where structure and logic don't flow, losing the reader's attention
• Places where there aren't transitions between paragraphs, confusing the reader
• Moments where your narrative or the arguments you're making are unclear

But pointing to potential problems is not the same as actually rewriting—editors let authors fix the problems themselves.

Bad editing is usually very heavy-handed editing. Instead of helping you find your best voice and ideas, a bad editor changes your writing into their own vision.

You may be dealing with a bad editor if they:

• Add material (examples, descriptions) that doesn't come from you
• Use a thesaurus to make your college essay sound "more mature"
• Add meaning or insight to the essay that doesn't come from you
• Tell you what to say and how to say it
• Write sentences, phrases, and paragraphs for you
• Change your voice in the essay so it no longer sounds like it was written by a teenager

Colleges can tell the difference between a 17-year-old's writing and a 50-year-old's writing. Not only that, they have access to your SAT or ACT Writing section, so they can compare your essay to something else you wrote. Writing that's a little more polished is great and expected. But a totally different voice and style will raise questions.

Where's the Line Between Helpful Editing and Unethical Over-Editing?

Sometimes it's hard to tell whether your college essay editor is doing the right thing. Here are some guidelines for staying on the ethical side of the line.

• An editor should say that the opening paragraph is kind of boring, and explain what exactly is making it drag. But it's overstepping for an editor to tell you exactly how to change it.
• An editor should point out where your prose is unclear or vague. But it's completely inappropriate for the editor to rewrite that section of your essay.
• An editor should let you know that a section is light on detail or description. But giving you similes and metaphors to beef up that description is a no-go.

Proofreading (also called copy-editing) is checking for errors in the last draft of a written work. It happens at the end of the process and is meant as the final polishing touch. Proofreading is meticulous and detail-oriented, focusing on small corrections. It sands off all the surface rough spots that could alienate the reader.

Because proofreading is usually concerned with making fixes on the word or sentence level, this is the only process where someone else can actually add to or take away things from your essay . This is because what they are adding or taking away tends to be one or two misplaced letters.

Laser focus. Proofreading is all about the tiny details, so the ability to really concentrate on finding small slip-ups is a must.

Excellent grammar and spelling skills. Proofreaders need to dot every "i" and cross every "t." Good proofreaders should correct spelling, punctuation, capitalization, and grammar. They should put foreign words in italics and surround quotations with quotation marks. They should check that you used the correct college's name, and that you adhered to any formatting requirements (name and date at the top of the page, uniform font and size, uniform spacing).

Limited interference. A proofreader needs to make sure that you followed any word limits. But if cuts need to be made to shorten the essay, that's your job and not the proofreader's.

A bad proofreader either tries to turn into an editor, or just lacks the skills and knowledge necessary to do the job.

Some signs that you're working with a bad proofreader are:

• If they suggest making major changes to the final draft of your essay. Proofreading happens when editing is already finished.
• If they aren't particularly good at spelling, or don't know grammar, or aren't detail-oriented enough to find someone else's small mistakes.
• If they start swapping out your words for fancier-sounding synonyms, or changing the voice and sound of your essay in other ways. A proofreader is there to check for errors, not to take the 17-year-old out of your writing.

What Do Colleges Think of Your Getting Help With Your Essay?

Admissions officers agree: light editing and proofreading are good—even required ! But they also want to make sure you're the one doing the work on your essay. They want essays with stories, voice, and themes that come from you. They want to see work that reflects your actual writing ability, and that focuses on what you find important.

On the Importance of Editing

Get feedback. Have a fresh pair of eyes give you some feedback. Don't allow someone else to rewrite your essay, but do take advantage of others' edits and opinions when they seem helpful. ( Bates College )

Read your essay aloud to someone. Reading the essay out loud offers a chance to hear how your essay sounds outside your head. This exercise reveals flaws in the essay's flow, highlights grammatical errors and helps you ensure that you are communicating the exact message you intended. ( Dickinson College )

On the Value of Proofreading

Share your essays with at least one or two people who know you well—such as a parent, teacher, counselor, or friend—and ask for feedback. Remember that you ultimately have control over your essays, and your essays should retain your own voice, but others may be able to catch mistakes that you missed and help suggest areas to cut if you are over the word limit. ( Yale University )

Proofread and then ask someone else to proofread for you. Although we want substance, we also want to be able to see that you can write a paper for our professors and avoid careless mistakes that would drive them crazy. ( Oberlin College )

On Watching Out for Too Much Outside Influence

Limit the number of people who review your essay. Too much input usually means your voice is lost in the writing style. ( Carleton College )

Ask for input (but not too much). Your parents, friends, guidance counselors, coaches, and teachers are great people to bounce ideas off of for your essay. They know how unique and spectacular you are, and they can help you decide how to articulate it. Keep in mind, however, that a 45-year-old lawyer writes quite differently from an 18-year-old student, so if your dad ends up writing the bulk of your essay, we're probably going to notice. ( Vanderbilt University )

Now let's talk about some potential people to approach for your college essay editing and proofreading needs. It's best to start close to home and slowly expand outward. Not only are your family and friends more invested in your success than strangers, but they also have a better handle on your interests and personality. This knowledge is key for judging whether your essay is expressing your true self.

Parents or Close Relatives

Your family may be full of potentially excellent editors! Parents are deeply committed to your well-being, and family members know you and your life well enough to offer details or incidents that can be included in your essay. On the other hand, the rewriting process necessarily involves criticism, which is sometimes hard to hear from someone very close to you.

A parent or close family member is a great choice for an editor if you can answer "yes" to the following questions. Is your parent or close relative a good writer or reader? Do you have a relationship where editing your essay won't create conflict? Are you able to constructively listen to criticism and suggestion from the parent?

One suggestion for defusing face-to-face discussions is to try working on the essay over email. Send your parent a draft, have them write you back some comments, and then you can pick which of their suggestions you want to use and which to discard.

Teachers or Tutors

A humanities teacher that you have a good relationship with is a great choice. I am purposefully saying humanities, and not just English, because teachers of Philosophy, History, Anthropology, and any other classes where you do a lot of writing, are all used to reviewing student work.

Moreover, any teacher or tutor that has been working with you for some time, knows you very well and can vet the essay to make sure it "sounds like you."

If your teacher or tutor has some experience with what college essays are supposed to be like, ask them to be your editor. If not, then ask whether they have time to proofread your final draft.

Guidance or College Counselor at Your School

The best thing about asking your counselor to edit your work is that this is their job. This means that they have a very good sense of what colleges are looking for in an application essay.

At the same time, school counselors tend to have relationships with admissions officers in many colleges, which again gives them insight into what works and which college is focused on what aspect of the application.

Unfortunately, in many schools the guidance counselor tends to be way overextended. If your ratio is 300 students to 1 college counselor, you're unlikely to get that person's undivided attention and focus. It is still useful to ask them for general advice about your potential topics, but don't expect them to be able to stay with your essay from first draft to final version.

Friends, Siblings, or Classmates

Although they most likely don't have much experience with what colleges are hoping to see, your peers are excellent sources for checking that your essay is you .

Friends and siblings are perfect for the read-aloud edit. Read your essay to them so they can listen for words and phrases that are stilted, pompous, or phrases that just don't sound like you.

You can even trade essays and give helpful advice on each other's work.

If your editor hasn't worked with college admissions essays very much, no worries! Any astute and attentive reader can still greatly help with your process. But, as in all things, beginners do better with some preparation.

First, your editor should read our advice about how to write a college essay introduction , how to spot and fix a bad college essay , and get a sense of what other students have written by going through some admissions essays that worked .

Then, as they read your essay, they can work through the following series of questions that will help them to guide you.

Introduction Questions

• Is the first sentence a killer opening line? Why or why not?
• Does the introduction hook the reader? Does it have a colorful, detailed, and interesting narrative? Or does it propose a compelling or surprising idea?
• Can you feel the author's voice in the introduction, or is the tone dry, dull, or overly formal? Show the places where the voice comes through.

Essay Body Questions

• Does the essay have a through-line? Is it built around a central argument, thought, idea, or focus? Can you put this idea into your own words?
• How is the essay organized? By logical progression? Chronologically? Do you feel order when you read it, or are there moments where you are confused or lose the thread of the essay?
• Does the essay have both narratives about the author's life and explanations and insight into what these stories reveal about the author's character, personality, goals, or dreams? If not, which is missing?
• Does the essay flow? Are there smooth transitions/clever links between paragraphs? Between the narrative and moments of insight?

Reader Response Questions

• Does the writer's personality come through? Do we know what the speaker cares about? Do we get a sense of "who he or she is"?
• Where did you feel most connected to the essay? Which parts of the essay gave you a "you are there" sensation by invoking your senses? What moments could you picture in your head well?
• Where are the details and examples vague and not specific enough?
• Did you get an "a-ha!" feeling anywhere in the essay? Is there a moment of insight that connected all the dots for you? Is there a good reveal or "twist" anywhere in the essay?
• What are the strengths of this essay? What needs the most improvement?

Should You Pay Money for Essay Editing?

One alternative to asking someone you know to help you with your college essay is the paid editor route. There are two different ways to pay for essay help: a private essay coach or a less personal editing service , like the many proliferating on the internet.

My advice is to think of these options as a last resort rather than your go-to first choice. I'll first go through the reasons why. Then, if you do decide to go with a paid editor, I'll help you decide between a coach and a service.

When to Consider a Paid Editor

In general, I think hiring someone to work on your essay makes a lot of sense if none of the people I discussed above are a possibility for you.

If you can't ask your parents. For example, if your parents aren't good writers, or if English isn't their first language. Or if you think getting your parents to help is going create unnecessary extra conflict in your relationship with them (applying to college is stressful as it is!)

If you can't ask your teacher or tutor. Maybe you don't have a trusted teacher or tutor that has time to look over your essay with focus. Or, for instance, your favorite humanities teacher has very limited experience with college essays and so won't know what admissions officers want to see.

If you can't ask your guidance counselor. This could be because your guidance counselor is way overwhelmed with other students.

If you can't share your essay with those who know you. It might be that your essay is on a very personal topic that you're unwilling to share with parents, teachers, or peers. Just make sure it doesn't fall into one of the bad-idea topics in our article on bad college essays .

If the cost isn't a consideration. Many of these services are quite expensive, and private coaches even more so. If you have finite resources, I'd say that hiring an SAT or ACT tutor (whether it's PrepScholar or someone else) is better way to spend your money . This is because there's no guarantee that a slightly better essay will sufficiently elevate the rest of your application, but a significantly higher SAT score will definitely raise your applicant profile much more.

Should You Hire an Essay Coach?

On the plus side, essay coaches have read dozens or even hundreds of college essays, so they have experience with the format. Also, because you'll be working closely with a specific person, it's more personal than sending your essay to a service, which will know even less about you.

But, on the minus side, you'll still be bouncing ideas off of someone who doesn't know that much about you . In general, if you can adequately get the help from someone you know, there is no advantage to paying someone to help you.

If you do decide to hire a coach, ask your school counselor, or older students that have used the service for recommendations. If you can't afford the coach's fees, ask whether they can work on a sliding scale —many do. And finally, beware those who guarantee admission to your school of choice—essay coaches don't have any special magic that can back up those promises.

Should You Send Your Essay to a Service?

On the plus side, essay editing services provide a similar product to essay coaches, and they cost significantly less . If you have some assurance that you'll be working with a good editor, the lack of face-to-face interaction won't prevent great results.

On the minus side, however, it can be difficult to gauge the quality of the service before working with them . If they are churning through many application essays without getting to know the students they are helping, you could end up with an over-edited essay that sounds just like everyone else's. In the worst case scenario, an unscrupulous service could send you back a plagiarized essay.

Getting recommendations from friends or a school counselor for reputable services is key to avoiding heavy-handed editing that writes essays for you or does too much to change your essay. Including a badly-edited essay like this in your application could cause problems if there are inconsistencies. For example, in interviews it might be clear you didn't write the essay, or the skill of the essay might not be reflected in your schoolwork and test scores.

Should You Buy an Essay Written by Someone Else?

Let me elaborate. There are super sketchy places on the internet where you can simply buy a pre-written essay. Don't do this!

For one thing, you'll be lying on an official, signed document. All college applications make you sign a statement saying something like this:

I certify that all information submitted in the admission process—including the application, the personal essay, any supplements, and any other supporting materials—is my own work, factually true, and honestly presented... I understand that I may be subject to a range of possible disciplinary actions, including admission revocation, expulsion, or revocation of course credit, grades, and degree, should the information I have certified be false. (From the Common Application )

For another thing, if your academic record doesn't match the essay's quality, the admissions officer will start thinking your whole application is riddled with lies.

Admission officers have full access to your writing portion of the SAT or ACT so that they can compare work that was done in proctored conditions with that done at home. They can tell if these were written by different people. Not only that, but there are now a number of search engines that faculty and admission officers can use to see if an essay contains strings of words that have appeared in other essays—you have no guarantee that the essay you bought wasn't also bought by 50 other students.

• You should get college essay help with both editing and proofreading
• A good editor will ask questions about your idea, logic, and structure, and will point out places where clarity is needed
• A good editor will absolutely not answer these questions, give you their own ideas, or write the essay or parts of the essay for you
• A good proofreader will find typos and check your formatting
• All of them agree that getting light editing and proofreading is necessary
• Parents, teachers, guidance or college counselor, and peers or siblings
• If you can't ask any of those, you can pay for college essay help, but watch out for services or coaches who over-edit you work
• Don't buy a pre-written essay! Colleges can tell, and it'll make your whole application sound false.

Ready to start working on your essay? Check out our explanation of the point of the personal essay and the role it plays on your applications and then explore our step-by-step guide to writing a great college essay .

Using the Common Application for your college applications? We have an excellent guide to the Common App essay prompts and useful advice on how to pick the Common App prompt that's right for you . Wondering how other people tackled these prompts? Then work through our roundup of over 130 real college essay examples published by colleges .

Stressed about whether to take the SAT again before submitting your application? Let us help you decide how many times to take this test . If you choose to go for it, we have the ultimate guide to studying for the SAT to give you the ins and outs of the best ways to study.

Anna scored in the 99th percentile on her SATs in high school, and went on to major in English at Princeton and to get her doctorate in English Literature at Columbia. She is passionate about improving student access to higher education.

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How to Write an Essay

Use the links below to jump directly to any section of this guide:

Essay Writing Fundamentals

How to prepare to write an essay, how to edit an essay, how to share and publish your essays, how to get essay writing help, how to find essay writing inspiration, resources for teaching essay writing.

Essays, short prose compositions on a particular theme or topic, are the bread and butter of academic life. You write them in class, for homework, and on standardized tests to show what you know. Unlike other kinds of academic writing (like the research paper) and creative writing (like short stories and poems), essays allow you to develop your original thoughts on a prompt or question. Essays come in many varieties: they can be expository (fleshing out an idea or claim), descriptive, (explaining a person, place, or thing), narrative (relating a personal experience), or persuasive (attempting to win over a reader). This guide is a collection of dozens of links about academic essay writing that we have researched, categorized, and annotated in order to help you improve your essay writing. 

Essays are different from other forms of writing; in turn, there are different kinds of essays. This section contains general resources for getting to know the essay and its variants. These resources introduce and define the essay as a genre, and will teach you what to expect from essay-based assessments.

Purdue OWL Online Writing Lab

One of the most trusted academic writing sites, Purdue OWL provides a concise introduction to the four most common types of academic essays.

"The Essay: History and Definition" (ThoughtCo)

This snappy article from ThoughtCo talks about the origins of the essay and different kinds of essays you might be asked to write. 

"What Is An Essay?" Video Lecture (Coursera)

The University of California at Irvine's free video lecture, available on Coursera, tells  you everything you need to know about the essay.

Wikipedia Article on the "Essay"

Wikipedia's article on the essay is comprehensive, providing both English-language and global perspectives on the essay form. Learn about the essay's history, forms, and styles.

"Understanding College and Academic Writing" (Aims Online Writing Lab)

This list of common academic writing assignments (including types of essay prompts) will help you know what to expect from essay-based assessments.

Before you start writing your essay, you need to figure out who you're writing for (audience), what you're writing about (topic/theme), and what you're going to say (argument and thesis). This section contains links to handouts, chapters, videos and more to help you prepare to write an essay.

How to Identify Your Audience

"Audience" (Univ. of North Carolina Writing Center)

This handout provides questions you can ask yourself to determine the audience for an academic writing assignment. It also suggests strategies for fitting your paper to your intended audience.

"Purpose, Audience, Tone, and Content" (Univ. of Minnesota Libraries)

This extensive book chapter from Writing for Success , available online through Minnesota Libraries Publishing, is followed by exercises to try out your new pre-writing skills.

"Determining Audience" (Aims Online Writing Lab)

This guide from a community college's writing center shows you how to know your audience, and how to incorporate that knowledge in your thesis statement.

"Know Your Audience" ( Paper Rater Blog)

This short blog post uses examples to show how implied audiences for essays differ. It reminds you to think of your instructor as an observer, who will know only the information you pass along.

How to Choose a Theme or Topic

"Research Tutorial: Developing Your Topic" (YouTube)

Take a look at this short video tutorial from the University of North Carolina at Chapel Hill to understand the basics of developing a writing topic.

"How to Choose a Paper Topic" (WikiHow)

This simple, step-by-step guide (with pictures!) walks you through choosing a paper topic. It starts with a detailed description of brainstorming and ends with strategies to refine your broad topic.

"How to Read an Assignment: Moving From Assignment to Topic" (Harvard College Writing Center)

Did your teacher give you a prompt or other instructions? This guide helps you understand the relationship between an essay assignment and your essay's topic.

"Guidelines for Choosing a Topic" (CliffsNotes)

This study guide from CliffsNotes both discusses how to choose a topic and makes a useful distinction between "topic" and "thesis."

How to Come Up with an Argument

"Argument" (Univ. of North Carolina Writing Center)

Not sure what "argument" means in the context of academic writing? This page from the University of North Carolina is a good place to start.

"The Essay Guide: Finding an Argument" (Study Hub)

This handout explains why it's important to have an argument when beginning your essay, and provides tools to help you choose a viable argument.

"Writing a Thesis and Making an Argument" (University of Iowa)

This page from the University of Iowa's Writing Center contains exercises through which you can develop and refine your argument and thesis statement.

"Developing a Thesis" (Harvard College Writing Center)

This page from Harvard's Writing Center collates some helpful dos and don'ts of argumentative writing, from steps in constructing a thesis to avoiding vague and confrontational thesis statements.

"Suggestions for Developing Argumentative Essays" (Berkeley Student Learning Center)

This page offers concrete suggestions for each stage of the essay writing process, from topic selection to drafting and editing. 

How to Outline your Essay

"Outlines" (Univ. of North Carolina at Chapel Hill via YouTube)

This short video tutorial from the University of North Carolina at Chapel Hill shows how to group your ideas into paragraphs or sections to begin the outlining process.

"Essay Outline" (Univ. of Washington Tacoma)

This two-page handout by a university professor simply defines the parts of an essay and then organizes them into an example outline.

"Types of Outlines and Samples" (Purdue OWL Online Writing Lab)

Purdue OWL gives examples of diverse outline strategies on this page, including the alphanumeric, full sentence, and decimal styles. 

"Outlining" (Harvard College Writing Center)

Once you have an argument, according to this handout, there are only three steps in the outline process: generalizing, ordering, and putting it all together. Then you're ready to write!

"Writing Essays" (Plymouth Univ.)

This packet, part of Plymouth University's Learning Development series, contains descriptions and diagrams relating to the outlining process.

"How to Write A Good Argumentative Essay: Logical Structure" (Criticalthinkingtutorials.com via YouTube)

This longer video tutorial gives an overview of how to structure your essay in order to support your argument or thesis. It is part of a longer course on academic writing hosted on Udemy.

Now that you've chosen and refined your topic and created an outline, use these resources to complete the writing process. Most essays contain introductions (which articulate your thesis statement), body paragraphs, and conclusions. Transitions facilitate the flow from one paragraph to the next so that support for your thesis builds throughout the essay. Sources and citations show where you got the evidence to support your thesis, which ensures that you avoid plagiarism. 

How to Write an Introduction

"Introductions" (Univ. of North Carolina Writing Center)

This page identifies the role of the introduction in any successful paper, suggests strategies for writing introductions, and warns against less effective introductions.

"How to Write A Good Introduction" (Michigan State Writing Center)

Beginning with the most common missteps in writing introductions, this guide condenses the essentials of introduction composition into seven points.

"The Introductory Paragraph" (ThoughtCo)

This blog post from academic advisor and college enrollment counselor Grace Fleming focuses on ways to grab your reader's attention at the beginning of your essay.

"Introductions and Conclusions" (Univ. of Toronto)

This guide from the University of Toronto gives advice that applies to writing both introductions and conclusions, including dos and don'ts.

"How to Write Better Essays: No One Does Introductions Properly" ( The Guardian )

This news article interviews UK professors on student essay writing; they point to introductions as the area that needs the most improvement.

How to Write a Thesis Statement

"Writing an Effective Thesis Statement" (YouTube)

This short, simple video tutorial from a college composition instructor at Tulsa Community College explains what a thesis statement is and what it does. 

"Thesis Statement: Four Steps to a Great Essay" (YouTube)

This fantastic tutorial walks you through drafting a thesis, using an essay prompt on Nathaniel Hawthorne's The Scarlet Letter as an example.

"How to Write a Thesis Statement" (WikiHow)

This step-by-step guide (with pictures!) walks you through coming up with, writing, and editing a thesis statement. It invites you think of your statement as a "working thesis" that can change.

"How to Write a Thesis Statement" (Univ. of Indiana Bloomington)

Ask yourself the questions on this page, part of Indiana Bloomington's Writing Tutorial Services, when you're writing and refining your thesis statement.

"Writing Tips: Thesis Statements" (Univ. of Illinois Center for Writing Studies)

This page gives plentiful examples of good to great thesis statements, and offers questions to ask yourself when formulating a thesis statement.

How to Write Body Paragraphs

"Body Paragraph" (Brightstorm)

This module of a free online course introduces you to the components of a body paragraph. These include the topic sentence, information, evidence, and analysis.

"Strong Body Paragraphs" (Washington Univ.)

This handout from Washington's Writing and Research Center offers in-depth descriptions of the parts of a successful body paragraph.

"Guide to Paragraph Structure" (Deakin Univ.)

This handout is notable for color-coding example body paragraphs to help you identify the functions various sentences perform.

"Writing Body Paragraphs" (Univ. of Minnesota Libraries)

The exercises in this section of Writing for Success  will help you practice writing good body paragraphs. It includes guidance on selecting primary support for your thesis.

"The Writing Process—Body Paragraphs" (Aims Online Writing Lab)

The information and exercises on this page will familiarize you with outlining and writing body paragraphs, and includes links to more information on topic sentences and transitions.

"The Five-Paragraph Essay" (ThoughtCo)

This blog post discusses body paragraphs in the context of one of the most common academic essay types in secondary schools.

How to Use Transitions

"Transitions" (Univ. of North Carolina Writing Center)

This page from the University of North Carolina at Chapel Hill explains what a transition is, and how to know if you need to improve your transitions.

"Using Transitions Effectively" (Washington Univ.)

This handout defines transitions, offers tips for using them, and contains a useful list of common transitional words and phrases grouped by function.

"Transitions" (Aims Online Writing Lab)

This page compares paragraphs without transitions to paragraphs with transitions, and in doing so shows how important these connective words and phrases are.

"Transitions in Academic Essays" (Scribbr)

This page lists four techniques that will help you make sure your reader follows your train of thought, including grouping similar information and using transition words.

"Transitions" (El Paso Community College)

This handout shows example transitions within paragraphs for context, and explains how transitions improve your essay's flow and voice.

"Make Your Paragraphs Flow to Improve Writing" (ThoughtCo)

This blog post, another from academic advisor and college enrollment counselor Grace Fleming, talks about transitions and other strategies to improve your essay's overall flow.

"Transition Words" (smartwords.org)

This handy word bank will help you find transition words when you're feeling stuck. It's grouped by the transition's function, whether that is to show agreement, opposition, condition, or consequence.

How to Write a Conclusion

"Parts of An Essay: Conclusions" (Brightstorm)

This module of a free online course explains how to conclude an academic essay. It suggests thinking about the "3Rs": return to hook, restate your thesis, and relate to the reader.

"Essay Conclusions" (Univ. of Maryland University College)

This overview of the academic essay conclusion contains helpful examples and links to further resources for writing good conclusions.

"How to End An Essay" (WikiHow)

This step-by-step guide (with pictures!) by an English Ph.D. walks you through writing a conclusion, from brainstorming to ending with a flourish.

"Ending the Essay: Conclusions" (Harvard College Writing Center)

This page collates useful strategies for writing an effective conclusion, and reminds you to "close the discussion without closing it off" to further conversation.

How to Include Sources and Citations

"Research and Citation Resources" (Purdue OWL Online Writing Lab)

Purdue OWL streamlines information about the three most common referencing styles (MLA, Chicago, and APA) and provides examples of how to cite different resources in each system.

EasyBib: Free Bibliography Generator

This online tool allows you to input information about your source and automatically generate citations in any style. Be sure to select your resource type before clicking the "cite it" button.

CitationMachine

Like EasyBib, this online tool allows you to input information about your source and automatically generate citations in any style. 

Modern Language Association Handbook (MLA)

Here, you'll find the definitive and up-to-date record of MLA referencing rules. Order through the link above, or check to see if your library has a copy.

Chicago Manual of Style

Here, you'll find the definitive and up-to-date record of Chicago referencing rules. You can take a look at the table of contents, then choose to subscribe or start a free trial.

How to Avoid Plagiarism

"What is Plagiarism?" (plagiarism.org)

This nonprofit website contains numerous resources for identifying and avoiding plagiarism, and reminds you that even common activities like copying images from another website to your own site may constitute plagiarism.

"Plagiarism" (University of Oxford)

This interactive page from the University of Oxford helps you check for plagiarism in your work, making it clear how to avoid citing another person's work without full acknowledgement.

"Avoiding Plagiarism" (MIT Comparative Media Studies)

This quick guide explains what plagiarism is, what its consequences are, and how to avoid it. It starts by defining three words—quotation, paraphrase, and summary—that all constitute citation.

"Harvard Guide to Using Sources" (Harvard Extension School)

This comprehensive website from Harvard brings together articles, videos, and handouts about referencing, citation, and plagiarism. 

Grammarly contains tons of helpful grammar and writing resources, including a free tool to automatically scan your essay to check for close affinities to published work. 

Noplag is another popular online tool that automatically scans your essay to check for signs of plagiarism. Simply copy and paste your essay into the box and click "start checking."

Once you've written your essay, you'll want to edit (improve content), proofread (check for spelling and grammar mistakes), and finalize your work until you're ready to hand it in. This section brings together tips and resources for navigating the editing process. 

"Writing a First Draft" (Academic Help)

This is an introduction to the drafting process from the site Academic Help, with tips for getting your ideas on paper before editing begins.

"Editing and Proofreading" (Univ. of North Carolina Writing Center)

This page provides general strategies for revising your writing. They've intentionally left seven errors in the handout, to give you practice in spotting them.

"How to Proofread Effectively" (ThoughtCo)

This article from ThoughtCo, along with those linked at the bottom, help describe common mistakes to check for when proofreading.

"7 Simple Edits That Make Your Writing 100% More Powerful" (SmartBlogger)

This blog post emphasizes the importance of powerful, concise language, and reminds you that even your personal writing heroes create clunky first drafts.

"Editing Tips for Effective Writing" (Univ. of Pennsylvania)

On this page from Penn's International Relations department, you'll find tips for effective prose, errors to watch out for, and reminders about formatting.

"Editing the Essay" (Harvard College Writing Center)

This article, the first of two parts, gives you applicable strategies for the editing process. It suggests reading your essay aloud, removing any jargon, and being unafraid to remove even "dazzling" sentences that don't belong.

"Guide to Editing and Proofreading" (Oxford Learning Institute)

This handout from Oxford covers the basics of editing and proofreading, and reminds you that neither task should be rushed. 

In addition to plagiarism-checkers, Grammarly has a plug-in for your web browser that checks your writing for common mistakes.

After you've prepared, written, and edited your essay, you might want to share it outside the classroom. This section alerts you to print and web opportunities to share your essays with the wider world, from online writing communities and blogs to published journals geared toward young writers.

Sharing Your Essays Online

Go Teen Writers

Go Teen Writers is an online community for writers aged 13 - 19. It was founded by Stephanie Morrill, an author of contemporary young adult novels. 

Tumblr is a blogging website where you can share your writing and interact with other writers online. It's easy to add photos, links, audio, and video components.

Writersky provides an online platform for publishing and reading other youth writers' work. Its current content is mostly devoted to fiction.

Publishing Your Essays Online

This teen literary journal publishes in print, on the web, and (more frequently), on a blog. It is committed to ensuring that "teens see their authentic experience reflected on its pages."

The Matador Review

This youth writing platform celebrates "alternative," unconventional writing. The link above will take you directly to the site's "submissions" page.

Teen Ink has a website, monthly newsprint magazine, and quarterly poetry magazine promoting the work of young writers.

The largest online reading platform, Wattpad enables you to publish your work and read others' work. Its inline commenting feature allows you to share thoughts as you read along.

Publishing Your Essays in Print

Canvas Teen Literary Journal

This quarterly literary magazine is published for young writers by young writers. They accept many kinds of writing, including essays.

The Claremont Review

This biannual international magazine, first published in 1992, publishes poetry, essays, and short stories from writers aged 13 - 19.

Skipping Stones

This young writers magazine, founded in 1988, celebrates themes relating to ecological and cultural diversity. It publishes poems, photos, articles, and stories.

The Telling Room

This nonprofit writing center based in Maine publishes children's work on their website and in book form. The link above directs you to the site's submissions page.

Essay Contests

Scholastic Arts and Writing Awards

This prestigious international writing contest for students in grades 7 - 12 has been committed to "supporting the future of creativity since 1923."

Society of Professional Journalists High School Essay Contest

An annual essay contest on the theme of journalism and media, the Society of Professional Journalists High School Essay Contest awards scholarships up to $1,000.

National YoungArts Foundation

Here, you'll find information on a government-sponsored writing competition for writers aged 15 - 18. The foundation welcomes submissions of creative nonfiction, novels, scripts, poetry, short story and spoken word.

Signet Classics Student Scholarship Essay Contest

With prompts on a different literary work each year, this competition from Signet Classics awards college scholarships up to $1,000.

"The Ultimate Guide to High School Essay Contests" (CollegeVine)

See this handy guide from CollegeVine for a list of more competitions you can enter with your academic essay, from the National Council of Teachers of English Achievement Awards to the National High School Essay Contest by the U.S. Institute of Peace.

Whether you're struggling to write academic essays or you think you're a pro, there are workshops and online tools that can help you become an even better writer. Even the most seasoned writers encounter writer's block, so be proactive and look through our curated list of resources to combat this common frustration.

Online Essay-writing Classes and Workshops

"Getting Started with Essay Writing" (Coursera)

Coursera offers lots of free, high-quality online classes taught by college professors. Here's one example, taught by instructors from the University of California Irvine.

"Writing and English" (Brightstorm)

Brightstorm's free video lectures are easy to navigate by topic. This unit on the parts of an essay features content on the essay hook, thesis, supporting evidence, and more.

"How to Write an Essay" (EdX)

EdX is another open online university course website with several two- to five-week courses on the essay. This one is geared toward English language learners.

Writer's Digest University

This renowned writers' website offers online workshops and interactive tutorials. The courses offered cover everything from how to get started through how to get published.

Writing.com

Signing up for this online writer's community gives you access to helpful resources as well as an international community of writers.

How to Overcome Writer's Block

"Symptoms and Cures for Writer's Block" (Purdue OWL)

Purdue OWL offers a list of signs you might have writer's block, along with ways to overcome it. Consider trying out some "invention strategies" or ways to curb writing anxiety.

"Overcoming Writer's Block: Three Tips" ( The Guardian )

These tips, geared toward academic writing specifically, are practical and effective. The authors advocate setting realistic goals, creating dedicated writing time, and participating in social writing.

"Writing Tips: Strategies for Overcoming Writer's Block" (Univ. of Illinois)

This page from the University of Illinois at Urbana-Champaign's Center for Writing Studies acquaints you with strategies that do and do not work to overcome writer's block.

"Writer's Block" (Univ. of Toronto)

Ask yourself the questions on this page; if the answer is "yes," try out some of the article's strategies. Each question is accompanied by at least two possible solutions.

If you have essays to write but are short on ideas, this section's links to prompts, example student essays, and celebrated essays by professional writers might help. You'll find writing prompts from a variety of sources, student essays to inspire you, and a number of essay writing collections.

Essay Writing Prompts

"50 Argumentative Essay Topics" (ThoughtCo)

Take a look at this list and the others ThoughtCo has curated for different kinds of essays. As the author notes, "a number of these topics are controversial and that's the point."

"401 Prompts for Argumentative Writing" ( New York Times )

This list (and the linked lists to persuasive and narrative writing prompts), besides being impressive in length, is put together by actual high school English teachers.

"SAT Sample Essay Prompts" (College Board)

If you're a student in the U.S., your classroom essay prompts are likely modeled on the prompts in U.S. college entrance exams. Take a look at these official examples from the SAT.

"Popular College Application Essay Topics" (Princeton Review)

This page from the Princeton Review dissects recent Common Application essay topics and discusses strategies for answering them.

Example Student Essays

"501 Writing Prompts" (DePaul Univ.)

This nearly 200-page packet, compiled by the LearningExpress Skill Builder in Focus Writing Team, is stuffed with writing prompts, example essays, and commentary.

"Topics in English" (Kibin)

Kibin is a for-pay essay help website, but its example essays (organized by topic) are available for free. You'll find essays on everything from  A Christmas Carol  to perseverance.

"Student Writing Models" (Thoughtful Learning)

Thoughtful Learning, a website that offers a variety of teaching materials, provides sample student essays on various topics and organizes them by grade level.

"Five-Paragraph Essay" (ThoughtCo)

In this blog post by a former professor of English and rhetoric, ThoughtCo brings together examples of five-paragraph essays and commentary on the form.

The Best Essay Writing Collections

The Best American Essays of the Century by Joyce Carol Oates (Amazon)

This collection of American essays spanning the twentieth century was compiled by award winning author and Princeton professor Joyce Carol Oates.

The Best American Essays 2017 by Leslie Jamison (Amazon)

Leslie Jamison, the celebrated author of essay collection  The Empathy Exams , collects recent, high-profile essays into a single volume.

The Art of the Personal Essay by Phillip Lopate (Amazon)

Documentary writer Phillip Lopate curates this historical overview of the personal essay's development, from the classical era to the present.

The White Album by Joan Didion (Amazon)

This seminal essay collection was authored by one of the most acclaimed personal essayists of all time, American journalist Joan Didion.

Consider the Lobster by David Foster Wallace (Amazon)

Read this famous essay collection by David Foster Wallace, who is known for his experimentation with the essay form. He pushed the boundaries of personal essay, reportage, and political polemic.

"50 Successful Harvard Application Essays" (Staff of the The Harvard Crimson )

If you're looking for examples of exceptional college application essays, this volume from Harvard's daily student newspaper is one of the best collections on the market.

Are you an instructor looking for the best resources for teaching essay writing? This section contains resources for developing in-class activities and student homework assignments. You'll find content from both well-known university writing centers and online writing labs.

Essay Writing Classroom Activities for Students

"In-class Writing Exercises" (Univ. of North Carolina Writing Center)

This page lists exercises related to brainstorming, organizing, drafting, and revising. It also contains suggestions for how to implement the suggested exercises.

"Teaching with Writing" (Univ. of Minnesota Center for Writing)

Instructions and encouragement for using "freewriting," one-minute papers, logbooks, and other write-to-learn activities in the classroom can be found here.

"Writing Worksheets" (Berkeley Student Learning Center)

Berkeley offers this bank of writing worksheets to use in class. They are nested under headings for "Prewriting," "Revision," "Research Papers" and more.

"Using Sources and Avoiding Plagiarism" (DePaul University)

Use these activities and worksheets from DePaul's Teaching Commons when instructing students on proper academic citation practices.

Essay Writing Homework Activities for Students

"Grammar and Punctuation Exercises" (Aims Online Writing Lab)

These five interactive online activities allow students to practice editing and proofreading. They'll hone their skills in correcting comma splices and run-ons, identifying fragments, using correct pronoun agreement, and comma usage.

"Student Interactives" (Read Write Think)

Read Write Think hosts interactive tools, games, and videos for developing writing skills. They can practice organizing and summarizing, writing poetry, and developing lines of inquiry and analysis.

This free website offers writing and grammar activities for all grade levels. The lessons are designed to be used both for large classes and smaller groups.

"Writing Activities and Lessons for Every Grade" (Education World)

Education World's page on writing activities and lessons links you to more free, online resources for learning how to "W.R.I.T.E.": write, revise, inform, think, and edit.

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What is an Essay?

10 May, 2020

11 minutes read

Author:  Tomas White

Well, beyond a jumble of words usually around 2,000 words or so - what is an essay, exactly? Whether you’re taking English, sociology, history, biology, art, or a speech class, it’s likely you’ll have to write an essay or two. So how is an essay different than a research paper or a review? Let’s find out!

Defining the Term – What is an Essay?

The essay is a written piece that is designed to present an idea, propose an argument, express the emotion or initiate debate. It is a tool that is used to present writer’s ideas in a non-fictional way. Multiple applications of this type of writing go way beyond, providing political manifestos and art criticism as well as personal observations and reflections of the author.

An essay can be as short as 500 words, it can also be 5000 words or more.  However, most essays fall somewhere around 1000 to 3000 words ; this word range provides the writer enough space to thoroughly develop an argument and work to convince the reader of the author’s perspective regarding a particular issue.  The topics of essays are boundless: they can range from the best form of government to the benefits of eating peppermint leaves daily. As a professional provider of custom writing, our service has helped thousands of customers to turn in essays in various forms and disciplines.

Origins of the Essay

Over the course of more than six centuries essays were used to question assumptions, argue trivial opinions and to initiate global discussions. Let’s have a closer look into historical progress and various applications of this literary phenomenon to find out exactly what it is.

Today’s modern word “essay” can trace its roots back to the French “essayer” which translates closely to mean “to attempt” .  This is an apt name for this writing form because the essay’s ultimate purpose is to attempt to convince the audience of something.  An essay’s topic can range broadly and include everything from the best of Shakespeare’s plays to the joys of April.

The essay comes in many shapes and sizes; it can focus on a personal experience or a purely academic exploration of a topic.  Essays are classified as a subjective writing form because while they include expository elements, they can rely on personal narratives to support the writer’s viewpoint.  The essay genre includes a diverse array of academic writings ranging from literary criticism to meditations on the natural world.  Most typically, the essay exists as a shorter writing form; essays are rarely the length of a novel.  However, several historic examples, such as John Locke’s seminal work “An Essay Concerning Human Understanding” just shows that a well-organized essay can be as long as a novel.

The Essay in Literature

The essay enjoys a long and renowned history in literature.  They first began gaining in popularity in the early 16 th century, and their popularity has continued today both with original writers and ghost writers.  Many readers prefer this short form in which the writer seems to speak directly to the reader, presenting a particular claim and working to defend it through a variety of means.  Not sure if you’ve ever read a great essay? You wouldn’t believe how many pieces of literature are actually nothing less than essays, or evolved into more complex structures from the essay. Check out this list of literary favorites:

• The Book of My Lives by Aleksandar Hemon
• Notes of a Native Son by James Baldwin
• Against Interpretation by Susan Sontag
• High-Tide in Tucson: Essays from Now and Never by Barbara Kingsolver
• Slouching Toward Bethlehem by Joan Didion
• Naked by David Sedaris
• Walden; or, Life in the Woods by Henry David Thoreau

Pretty much as long as writers have had something to say, they’ve created essays to communicate their viewpoint on pretty much any topic you can think of!

The Essay in Academics

Not only are students required to read a variety of essays during their academic education, but they will likely be required to write several different kinds of essays throughout their scholastic career.  Don’t love to write?  Then consider working with a ghost essay writer !  While all essays require an introduction, body paragraphs in support of the argumentative thesis statement, and a conclusion, academic essays can take several different formats in the way they approach a topic.  Common essays required in high school, college, and post-graduate classes include:

Five paragraph essay

This is the most common type of a formal essay. The type of paper that students are usually exposed to when they first hear about the concept of the essay itself. It follows easy outline structure – an opening introduction paragraph; three body paragraphs to expand the thesis; and conclusion to sum it up.

Argumentative essay

These essays are commonly assigned to explore a controversial issue.  The goal is to identify the major positions on either side and work to support the side the writer agrees with while refuting the opposing side’s potential arguments.

Compare and Contrast essay

This essay compares two items, such as two poems, and works to identify similarities and differences, discussing the strength and weaknesses of each.  This essay can focus on more than just two items, however.  The point of this essay is to reveal new connections the reader may not have considered previously.

Definition essay

This essay has a sole purpose – defining a term or a concept in as much detail as possible. Sounds pretty simple, right? Well, not quite. The most important part of the process is picking up the word. Before zooming it up under the microscope, make sure to choose something roomy so you can define it under multiple angles. The definition essay outline will reflect those angles and scopes.

Descriptive essay

Perhaps the most fun to write, this essay focuses on describing its subject using all five of the senses.  The writer aims to fully describe the topic; for example, a descriptive essay could aim to describe the ocean to someone who’s never seen it or the job of a teacher.  Descriptive essays rely heavily on detail and the paragraphs can be organized by sense.

Illustration essay

The purpose of this essay is to describe an idea, occasion or a concept with the help of clear and vocal examples. “Illustration” itself is handled in the body paragraphs section. Each of the statements, presented in the essay needs to be supported with several examples. Illustration essay helps the author to connect with his audience by breaking the barriers with real-life examples – clear and indisputable.

Informative Essay

Being one the basic essay types, the informative essay is as easy as it sounds from a technical standpoint. High school is where students usually encounter with informative essay first time. The purpose of this paper is to describe an idea, concept or any other abstract subject with the help of proper research and a generous amount of storytelling.

Narrative essay

This type of essay focuses on describing a certain event or experience, most often chronologically.  It could be a historic event or an ordinary day or month in a regular person’s life. Narrative essay proclaims a free approach to writing it, therefore it does not always require conventional attributes, like the outline. The narrative itself typically unfolds through a personal lens, and is thus considered to be a subjective form of writing.

Persuasive essay

The purpose of the persuasive essay is to provide the audience with a 360-view on the concept idea or certain topic – to persuade the reader to adopt a certain viewpoint. The viewpoints can range widely from why visiting the dentist is important to why dogs make the best pets to why blue is the best color.  Strong, persuasive language is a defining characteristic of this essay type.

The Essay in Art

Several other artistic mediums have adopted the essay as a means of communicating with their audience.  In the visual arts, such as painting or sculpting, the rough sketches of the final product are sometimes deemed essays.  Likewise, directors may opt to create a film essay which is similar to a documentary in that it offers a personal reflection on a relevant issue.  Finally, photographers often create photographic essays in which they use a series of photographs to tell a story, similar to a narrative or a descriptive essay.

Drawing the line – question answered

“What is an Essay?” is quite a polarizing question. On one hand, it can easily be answered in a couple of words. On the other, it is surely the most profound and self-established type of content there ever was. Going back through the history of the last five-six centuries helps us understand where did it come from and how it is being applied ever since.

If you must write an essay, follow these five important steps to works towards earning the “A” you want:

• Understand and review the kind of essay you must write
• Brainstorm your argument
• Find research from reliable sources to support your perspective
• Cite all sources parenthetically within the paper and on the Works Cited page
• Follow all grammatical rules

Generally speaking, when you must write any type of essay, start sooner rather than later!  Don’t procrastinate – give yourself time to develop your perspective and work on crafting a unique and original approach to the topic.  Remember: it’s always a good idea to have another set of eyes (or three) look over your essay before handing in the final draft to your teacher or professor.  Don’t trust your fellow classmates?  Consider hiring an editor or a ghostwriter to help out!

If you are still unsure on whether you can cope with your task – you are in the right place to get help. HandMadeWriting is the perfect answer to the question “Who can write my essay?”

A life lesson in Romeo and Juliet taught by death

Due to human nature, we draw conclusions only when life gives us a lesson since the experience of others is not so effective and powerful. Therefore, when analyzing and sorting out common problems we face, we may trace a parallel with well-known book characters or real historical figures. Moreover, we often compare our situations with […]

Ethical Research Paper Topics

Writing a research paper on ethics is not an easy task, especially if you do not possess excellent writing skills and do not like to contemplate controversial questions. But an ethics course is obligatory in all higher education institutions, and students have to look for a way out and be creative. When you find an […]

Art Research Paper Topics

Students obtaining degrees in fine art and art & design programs most commonly need to write a paper on art topics. However, this subject is becoming more popular in educational institutions for expanding students’ horizons. Thus, both groups of receivers of education: those who are into arts and those who only get acquainted with art […]

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Essay Writing

Welcome to the Purdue OWL

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This resource begins with a general description of essay writing and moves to a discussion of common essay genres students may encounter across the curriculum. The four genres of essays (description, narration, exposition, and argumentation) are common paper assignments you may encounter in your writing classes. Although these genres, also known as the modes of discourse, have been criticized by some composition scholars, the Purdue OWL recognizes the wide spread use of these genres and students’ need to understand and produce these types of essays. We hope these resources will help.

The essay is a commonly assigned form of writing that every student will encounter while in academia. Therefore, it is wise for the student to become capable and comfortable with this type of writing early on in her training.

Essays can be a rewarding and challenging type of writing and are often assigned either to be done in class, which requires previous planning and practice (and a bit of creativity) on the part of the student, or as homework, which likewise demands a certain amount of preparation. Many poorly crafted essays have been produced on account of a lack of preparation and confidence. However, students can avoid the discomfort often associated with essay writing by understanding some common genres.

Before delving into its various genres, let’s begin with a basic definition of the essay.

What is an essay?

Though the word essay has come to be understood as a type of writing in Modern English, its origins provide us with some useful insights. The word comes into the English language through the French influence on Middle English; tracing it back further, we find that the French form of the word comes from the Latin verb exigere , which means "to examine, test, or (literally) to drive out." Through the excavation of this ancient word, we are able to unearth the essence of the academic essay: to encourage students to test or examine their ideas concerning a particular topic.

Essays are shorter pieces of writing that often require the student to hone a number of skills such as close reading, analysis, comparison and contrast, persuasion, conciseness, clarity, and exposition. As is evidenced by this list of attributes, there is much to be gained by the student who strives to succeed at essay writing.

The purpose of an essay is to encourage students to develop ideas and concepts in their writing with the direction of little more than their own thoughts (it may be helpful to view the essay as the converse of a research paper). Therefore, essays are (by nature) concise and require clarity in purpose and direction. This means that there is no room for the student’s thoughts to wander or stray from his or her purpose; the writing must be deliberate and interesting.

This handout should help students become familiar and comfortable with the process of essay composition through the introduction of some common essay genres.

This handout includes a brief introduction to the following genres of essay writing:

• Expository essays
• Descriptive essays
• Narrative essays
• Argumentative (Persuasive) essays

The writer of the academic essay aims to persuade readers of an idea based on evidence. The beginning of the essay is a crucial first step in this process. In order to engage readers and establish your authority, the beginning of your essay has to accomplish certain business. Your beginning should introduce the essay, focus it, and orient readers.

Introduce the Essay.  The beginning lets your readers know what the essay is about, the  topic . The essay's topic does not exist in a vacuum, however; part of letting readers know what your essay is about means establishing the essay's  context , the frame within which you will approach your topic. For instance, in an essay about the First Amendment guarantee of freedom of speech, the context may be a particular legal theory about the speech right; it may be historical information concerning the writing of the amendment; it may be a contemporary dispute over flag burning; or it may be a question raised by the text itself. The point here is that, in establishing the essay's context, you are also limiting your topic. That is, you are framing an approach to your topic that necessarily eliminates other approaches. Thus, when you determine your context, you simultaneously narrow your topic and take a big step toward focusing your essay. Here's an example.

The paragraph goes on. But as you can see, Chopin's novel (the topic) is introduced in the context of the critical and moral controversy its publication engendered.

Focus the Essay.  Beyond introducing your topic, your beginning must also let readers know what the central issue is. What question or problem will you be thinking about? You can pose a question that will lead to your idea (in which case, your idea will be the answer to your question), or you can make a thesis statement. Or you can do both: you can ask a question and immediately suggest the answer that your essay will argue. Here's an example from an essay about Memorial Hall.

The fullness of your idea will not emerge until your conclusion, but your beginning must clearly indicate the direction your idea will take, must set your essay on that road. And whether you focus your essay by posing a question, stating a thesis, or combining these approaches, by the end of your beginning, readers should know what you're writing about, and  why —and why they might want to read on.

Orient Readers.  Orienting readers, locating them in your discussion, means providing information and explanations wherever necessary for your readers' understanding. Orienting is important throughout your essay, but it is crucial in the beginning. Readers who don't have the information they need to follow your discussion will get lost and quit reading. (Your teachers, of course, will trudge on.) Supplying the necessary information to orient your readers may be as simple as answering the journalist's questions of who, what, where, when, how, and why. It may mean providing a brief overview of events or a summary of the text you'll be analyzing. If the source text is brief, such as the First Amendment, you might just quote it. If the text is well known, your summary, for most audiences, won't need to be more than an identifying phrase or two:

Often, however, you will want to summarize your source more fully so that readers can follow your analysis of it.

Questions of Length and Order.  How long should the beginning be? The length should be proportionate to the length and complexity of the whole essay. For instance, if you're writing a five-page essay analyzing a single text, your beginning should be brief, no more than one or two paragraphs. On the other hand, it may take a couple of pages to set up a ten-page essay.

Does the business of the beginning have to be addressed in a particular order? No, but the order should be logical. Usually, for instance, the question or statement that focuses the essay comes at the end of the beginning, where it serves as the jumping-off point for the middle, or main body, of the essay. Topic and context are often intertwined, but the context may be established before the particular topic is introduced. In other words, the order in which you accomplish the business of the beginning is flexible and should be determined by your purpose.

Opening Strategies.  There is still the further question of how to start. What makes a good opening? You can start with specific facts and information, a keynote quotation, a question, an anecdote, or an image. But whatever sort of opening you choose, it should be directly related to your focus. A snappy quotation that doesn't help establish the context for your essay or that later plays no part in your thinking will only mislead readers and blur your focus. Be as direct and specific as you can be. This means you should avoid two types of openings:

• The history-of-the-world (or long-distance) opening, which aims to establish a context for the essay by getting a long running start: "Ever since the dawn of civilized life, societies have struggled to reconcile the need for change with the need for order." What are we talking about here, political revolution or a new brand of soft drink? Get to it.
• The funnel opening (a variation on the same theme), which starts with something broad and general and "funnels" its way down to a specific topic. If your essay is an argument about state-mandated prayer in public schools, don't start by generalizing about religion; start with the specific topic at hand.

Remember.  After working your way through the whole draft, testing your thinking against the evidence, perhaps changing direction or modifying the idea you started with, go back to your beginning and make sure it still provides a clear focus for the essay. Then clarify and sharpen your focus as needed. Clear, direct beginnings rarely present themselves ready-made; they must be written, and rewritten, into the sort of sharp-eyed clarity that engages readers and establishes your authority.

Copyright 1999, Patricia Kain, for the Writing Center at Harvard University

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Guest Essay

A Chill Has Fallen Over Jews in Publishing

By James Kirchick

Mr. Kirchick is a contributing writer to Tablet magazine, a writer at large for Air Mail and the author of “Secret City: The Hidden History of Gay Washington.”

This month, an account on X with the handle @moyurireads and 360 followers published a link to a color-coded spreadsheet classifying nearly 200 writers according to their views on the “genocide” in Gaza. Titled “Is Your Fav Author a Zionist?,” it reads like a cross between Tiger Beat and “The Protocols of the Elders of Zion.”

The novelist Emily St. John Mandel, the author of “Station Eleven” and “Sea of Tranquility,” earned a red “pro-Israel/Zionist” classification because, according to the list’s creator, she “travels to Israel frequently talks favorably about it.” Simply for posting a link to the Israeli chapter of the Red Cross, the novelist Kristin Hannah was deemed a “Zionist,” as was the author Gabrielle Zevin for delivering a book talk to Hadassah, a Jewish women’s organization. Needless to say, the creator of the list — whose post on X announcing it garnered over a million views within a few days — encourages readers to boycott any works produced by “Zionists.”

The spreadsheet is but the crudest example of the virulently anti-Israel — and increasingly antisemitic — sentiment that has been coursing through the literary world since the Hamas massacre of Oct. 7. Much of it revolves around the charge of genocide and seeks to punish Zionists and anyone else who refuses to explicitly denounce the Jewish state for allegedly committing said crime. Since a large majority of American Jews (80 percent of whom, according to a 2020 poll , said that caring about Israel is an important or essential part of their Judaism) are Zionists, to accuse all Zionists of complicity in genocide is to anathematize a core component of Jewish identity.

Over the past several months, a litmus test has emerged across wide swaths of the literary world effectively excluding Jews from full participation unless they denounce Israel. This phenomenon has been unfolding in progressive spaces (academia, politics, cultural organizations) for quite some time. That it has now hit the rarefied, highbrow realm of publishing — where Jewish Americans have made enormous contributions and the vitality of which depends on intellectual pluralism and free expression — is particularly alarming.

As is always and everywhere the case, this growing antisemitism is concomitant with a rising illiberalism. Rarely, if ever, do writers express unanimity on a contentious political issue. We’re a naturally argumentative bunch who — at least in theory — answer only to our own consciences.

To compel them to express support or disapproval for a cause is one of the cruelest things a society can do to writers, whose role is to tell society what they believe, regardless of how popular the message may be. The drawing up of lists, in particular, is a tactic with a long and ignominious history, employed by the enemies of literature — and liberty — on both the left and the right. But the problem goes much deeper than a tyro blacklist targeting “Zionists.”

One of the greatest mass delusions of the 21st century is the belief that Israel is committing a genocide against Palestinians. This grotesque moral inversion — in which a genocidal terrorist organization that instigated a war with Israel by committing the largest massacre of Jews since the Holocaust is absolved of responsibility while the victim of Hamas’s attack is charged with perpetrating the worst crime known to man — began taking shape before Israel even launched its ground invasion of Gaza.

A charitable description of those imputing genocidal motivations to Israel is that they are ignorant, essentially believing the word to mean “large numbers of civilian casualties.” (Here it’s worth noting that the United Nations, to little notice, has significantly lowered its estimate of the number of women and children killed in Gaza.) For others, accusing Israel of genocide is an emotional outlet for expressing outrage at such a horrific loss of life. A third, more pessimistic, characterization of the ubiquitous genocide canard is that it is only the latest iteration of the ancient antisemitic blood libel, which held that Jews murdered gentile children in order to use their blood for religious rituals.

College students and professional activists using overheated and imprecise language to convey their strongly held beliefs is hardly uncommon, and much of the intemperate language being directed at Israel and its Zionist supporters can be attributed to the hyperbole that increasingly characterizes our political discourse. What should worry us more is when people who have dedicated their lives to the written word manipulate language for a political end, one that is stigmatizing Jews.

Nine days after the Oct. 7 attack, the popular website Literary Hub began publishing what has since become a near-daily torrent of agitprop invective against what it describes as the “rogue ethnostate” of Israel, which it routinely accuses of committing genocide. In March, after a mass resignation of its staff members , the literary magazine Guernica retracted a personal essay by a left-wing Israeli woman about her experience volunteering to drive Palestinian children to Israel for medical treatment. In her resignation letter, one of the magazine’s co-publishers denounced the piece as “a hand-wringing apologia for Zionism and the ongoing genocide in Palestine.”

Whereas antisemitism in the literary world used to lurk in the shadows, according to the Jewish Book Council’s chief executive, Naomi Firestone-Teeter, since Oct. 7, it has become increasingly overt. “The fact that people have felt so proud and open about it is a different beast entirely,” she said. One of the most disturbing developments in this regard has been the frequency and contempt with which the word “Zionist” is now spit from people’s mouths in the United States.

Until relatively recently, the use of “Zionist” as a slur was most commonly confined to Soviet and Arab propagandists, who spent decades trying to render the word the moral equivalent of “Nazi.” Today many progressives use the word in similar fashion, making no distinction between a Zionist who supports a two-state solution (which, presumably, most Jews in the overwhelmingly liberal literary world do) and one who believes in a “Greater Israel” encompassing the entirety of the West Bank and Gaza Strip. And while anyone can be a Zionist, I’ve found in my 20 years of reporting on antisemitism that many Jews essentially hear “Jew” when someone shouts “Zionist" at them.

The corruption of the words “genocide” and “Zionist” lies at the root of the controversy threatening to unravel PEN America, the storied writers’ organization. As with many a literary contretemps, it involves a cascade of open letters. In February a missive that gained almost 1,500 signatures was published demanding that PEN “wake up from its own silent, tepid, neither-here-nor-there, self-congratulatory middle of the road and take an actual stand against an actual genocide.” The dozens of statements PEN had issued by that time calling attention to the plight of writers in Gaza (who the letter, without citing evidence, claimed had been “targeted” by Israel for assassination) were insufficient. “We demand PEN America release an official statement” about the writers killed in Gaza the letter read, “and name their murderer: Israel, a Zionist colonial state funded by the U.S. government.”

On March 20, PEN acceded to the ultimatum that it endorse the call for a cease-fire. But that did not satiate its critics.

Last month, in advance of PEN’s annual literary awards ceremony, nearly half of the nominated writers withdrew from the competition. A subset of those writers then released another open letter , declaring, “Among writers of conscience, there is no disagreement. There is fact and fiction. The fact is that Israel is leading a genocide of the Palestinian people.” They accused PEN of “normalizing genocide,” denounced PEN for its “platforming of Zionists” and, most shamefully, called for the resignation of its Jewish chief executive, Suzanne Nossel, on account of her “longstanding commitments to Zionism.”

Along with eight other past presidents of PEN, Salman Rushdie signed a letter in defense of the organization , an intervention that earned him an “unclear” rating on the anti-Zionist blacklist. (He has braved far worse from Islamist zealots and their Western apologists.) PEN ultimately canceled both the awards ceremony and subsequent World Voices Festival.

Dissatisfaction with PEN’s purported lack of indignation over the deaths of Palestinian writers is a fig leaf. Where were the efforts by those now decrying PEN to protest the complete absence of freedom of expression that has characterized the Gaza Strip under 17 years of Hamas rule?

The real objectives behind the cynical weaponization of the word “genocide” and the authoritarian insistence that anyone who disagrees with it is an enabler of one are to shut down debate, defame dissenters and impose a rigid orthodoxy throughout the publishing world. It is a naked attempt to impose an ideological litmus test on anyone hoping to join the republic of letters — a litmus test that the vast majority of Jews would fail.

A campaign of intimidation, the sort of thing that happens to the dissident writers in closed societies whom PEN regularly champions, is afoot to pressure writers into toeing this new party line. PEN’s current president, Jenny Finney Boylan, recently said that she had heard from “many, many authors who do not agree with those withdrawing from PEN events and who do not wish to withdraw from our events themselves but are afraid of the consequences if they speak up.”

Compelling speech — which is ultimately what PEN’s critics are demanding of it — is the tactic of commissars, not writers in a free society. Censorship, thought policing and bullying are antithetical to the spirit of literature, which is best understood as an intimate conversation between the author and individual readers.

PEN’s detractors aren’t helping the Palestinian people with their whitewashing of Hamas. They’re engaged in a hostile takeover of a noble organization committed to the defense of free expression in order to advance a sectarian and bigoted political agenda.

Neil Gaiman, Taylor Jenkins-Reid, Ms. Mandel and other hugely successful authors need not worry that being denounced as a Zionist will hurt their careers. But the blacklists and the boycotts do not really target them. The actual targets of this crusade are lesser-known authors, budding novelists, aspiring poets and creative writing students — largely but not exclusively Jewish — who can feel a change in the air.

“I do now definitely have concern as a Jewish author — two years working on a novel that has absolutely nothing to do with Jews in any way, just because it says ‘National Jewish Book Award winner’ in my bio — that it may change the way readers see the work,” said a Jewish creative writing professor and novelist who spoke to me on the condition of being quoted anonymously.

No longer is being on the receiving end of a review bomb the worst fate that can befall a Jewish writer exploring Jewish themes; even getting such a book published is becoming increasingly difficult. “It’s very clear you have to have real courage to acquire and publish proudly Jewish voices and books about being Jewish,” a prominent literary agent told me. “When you are seen as genocidal, a moral insult to humanity because you believe in Israel’s right to exist, you are now seen as deserving of being canceled.”

There’s a distasteful irony in a literary community that has gone to the barricades fighting book “bans” now rallying to boycott authors based on their ethnoreligious identity. For a growing set of writers, declaring one’s belief that the world’s only Jewish state is a genocidal entity whose dismantlement is necessary for the advancement of humankind is a political fashion statement, a bauble one parades around in order to signify being on the right team. As was Stalinism for an earlier generation of left-wing literary intellectuals, so is antisemitism becoming the avant-garde.

James Kirchick is a contributing writer to Tablet magazine, a writer at large for Air Mail and the author of “Secret City: The Hidden History of Gay Washington.”

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• Published: 03 June 2024

Applying large language models for automated essay scoring for non-native Japanese

• Wenchao Li 1 &
• Haitao Liu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  723 ( 2024 ) Cite this article

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Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

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Conventional machine learning technology in aes.

AES has experienced significant growth with the advancement of machine learning technologies in recent decades. In the earlier stages of AES development, conventional machine learning-based approaches were commonly used. These approaches involved the following procedures: a) feeding the machine with a dataset. In this step, a dataset of essays is provided to the machine learning system. The dataset serves as the basis for training the model and establishing patterns and correlations between linguistic features and human ratings. b) the machine learning model is trained using linguistic features that best represent human ratings and can effectively discriminate learners’ writing proficiency. These features include lexical richness (Lu, 2012 ; Kyle and Crossley, 2015 ; Kyle et al. 2021 ), syntactic complexity (Lu, 2010 ; Liu, 2008 ), text cohesion (Crossley and McNamara, 2016 ), and among others. Conventional machine learning approaches in AES require human intervention, such as manual correction and annotation of essays. This human involvement was necessary to create a labeled dataset for training the model. Several AES systems have been developed using conventional machine learning technologies. These include the Intelligent Essay Assessor (Landauer et al. 2003 ), the e-rater engine by Educational Testing Service (Attali and Burstein, 2006 ; Burstein, 2003 ), MyAccess with the InterlliMetric scoring engine by Vantage Learning (Elliot, 2003 ), and the Bayesian Essay Test Scoring system (Rudner and Liang, 2002 ). These systems have played a significant role in automating the essay scoring process and providing quick and consistent feedback to learners. However, as touched upon earlier, conventional machine learning approaches rely on predetermined linguistic features and often require manual intervention, making them less flexible and potentially limiting their generalizability to different contexts.

In the context of the Japanese language, conventional machine learning-incorporated AES tools include Jess (Ishioka and Kameda, 2006 ) and JWriter (Lee and Hasebe, 2017 ). Jess assesses essays by deducting points from the perfect score, utilizing the Mainichi Daily News newspaper as a database. The evaluation criteria employed by Jess encompass various aspects, such as rhetorical elements (e.g., reading comprehension, vocabulary diversity, percentage of complex words, and percentage of passive sentences), organizational structures (e.g., forward and reverse connection structures), and content analysis (e.g., latent semantic indexing). JWriter employs linear regression analysis to assign weights to various measurement indices, such as average sentence length and total number of characters. These weights are then combined to derive the overall score. A pilot study involving the Jess model was conducted on 1320 essays at different proficiency levels, including primary, intermediate, and advanced. However, the results indicated that the Jess model failed to significantly distinguish between these essay levels. Out of the 16 measures used, four measures, namely median sentence length, median clause length, median number of phrases, and maximum number of phrases, did not show statistically significant differences between the levels. Additionally, two measures exhibited between-level differences but lacked linear progression: the number of attributives declined words and the Kanji/kana ratio. On the other hand, the remaining measures, including maximum sentence length, maximum clause length, number of attributive conjugated words, maximum number of consecutive infinitive forms, maximum number of conjunctive-particle clauses, k characteristic value, percentage of big words, and percentage of passive sentences, demonstrated statistically significant between-level differences and displayed linear progression.

Both Jess and JWriter exhibit notable limitations, including the manual selection of feature parameters and weights, which can introduce biases into the scoring process. The reliance on human annotators to label non-native language essays also introduces potential noise and variability in the scoring. Furthermore, an important concern is the possibility of system manipulation and cheating by learners who are aware of the regression equation utilized by the models (Hirao et al. 2020 ). These limitations emphasize the need for further advancements in AES systems to address these challenges.

Deep learning technology in AES

Deep learning has emerged as one of the approaches for improving the accuracy and effectiveness of AES. Deep learning-based AES methods utilize artificial neural networks that mimic the human brain’s functioning through layered algorithms and computational units. Unlike conventional machine learning, deep learning autonomously learns from the environment and past errors without human intervention. This enables deep learning models to establish nonlinear correlations, resulting in higher accuracy. Recent advancements in deep learning have led to the development of transformers, which are particularly effective in learning text representations. Noteworthy examples include bidirectional encoder representations from transformers (BERT) (Devlin et al. 2019 ) and the generative pretrained transformer (GPT) (OpenAI).

BERT is a linguistic representation model that utilizes a transformer architecture and is trained on two tasks: masked linguistic modeling and next-sentence prediction (Hirao et al. 2020 ; Vaswani et al. 2017 ). In the context of AES, BERT follows specific procedures, as illustrated in Fig. 1 : (a) the tokenized prompts and essays are taken as input; (b) special tokens, such as [CLS] and [SEP], are added to mark the beginning and separation of prompts and essays; (c) the transformer encoder processes the prompt and essay sequences, resulting in hidden layer sequences; (d) the hidden layers corresponding to the [CLS] tokens (T[CLS]) represent distributed representations of the prompts and essays; and (e) a multilayer perceptron uses these distributed representations as input to obtain the final score (Hirao et al. 2020 ).

AES system with BERT (Hirao et al. 2020 ).

The training of BERT using a substantial amount of sentence data through the Masked Language Model (MLM) allows it to capture contextual information within the hidden layers. Consequently, BERT is expected to be capable of identifying artificial essays as invalid and assigning them lower scores (Mizumoto and Eguchi, 2023 ). In the context of AES for nonnative Japanese learners, Hirao et al. ( 2020 ) combined the long short-term memory (LSTM) model proposed by Hochreiter and Schmidhuber ( 1997 ) with BERT to develop a tailored automated Essay Scoring System. The findings of their study revealed that the BERT model outperformed both the conventional machine learning approach utilizing character-type features such as “kanji” and “hiragana”, as well as the standalone LSTM model. Takeuchi et al. ( 2021 ) presented an approach to Japanese AES that eliminates the requirement for pre-scored essays by relying solely on reference texts or a model answer for the essay task. They investigated multiple similarity evaluation methods, including frequency of morphemes, idf values calculated on Wikipedia, LSI, LDA, word-embedding vectors, and document vectors produced by BERT. The experimental findings revealed that the method utilizing the frequency of morphemes with idf values exhibited the strongest correlation with human-annotated scores across different essay tasks. The utilization of BERT in AES encounters several limitations. Firstly, essays often exceed the model’s maximum length limit. Second, only score labels are available for training, which restricts access to additional information.

Mizumoto and Eguchi ( 2023 ) were pioneers in employing the GPT model for AES in non-native English writing. Their study focused on evaluating the accuracy and reliability of AES using the GPT-3 text-davinci-003 model, analyzing a dataset of 12,100 essays from the corpus of nonnative written English (TOEFL11). The findings indicated that AES utilizing the GPT-3 model exhibited a certain degree of accuracy and reliability. They suggest that GPT-3-based AES systems hold the potential to provide support for human ratings. However, applying GPT model to AES presents a unique natural language processing (NLP) task that involves considerations such as nonnative language proficiency, the influence of the learner’s first language on the output in the target language, and identifying linguistic features that best indicate writing quality in a specific language. These linguistic features may differ morphologically or syntactically from those present in the learners’ first language, as observed in (1)–(3).

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

1 sg .-give. past- him-one .cl- book

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

Kare-ni-hon-o-age-mashi-ta

3 sg .- dat -hon- acc- give.honorification. past

Inflectional

give, give-s, gave, given, giving

Additionally, the morphological agglutination and subject-object-verb (SOV) order in Japanese, along with its idiomatic expressions, pose additional challenges for applying language models in AES tasks (4).

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

“My leg became like a stick (I am extremely tired).”

The example sentence provided demonstrates the morpho-syntactic structure of Japanese and the presence of an idiomatic expression. In this sentence, the verb “なる” (naru), meaning “to become”, appears at the end of the sentence. The verb stem “なり” (nari) is attached with morphemes indicating honorification (“ます” - mashu) and tense (“た” - ta), showcasing agglutination. While the sentence can be literally translated as “my leg became like a stick”, it carries an idiomatic interpretation that implies “I am extremely tired”.

To overcome this issue, CyberAgent Inc. ( 2023 ) has developed the Open-Calm series of language models specifically designed for Japanese. Open-Calm consists of pre-trained models available in various sizes, such as Small, Medium, Large, and 7b. Figure 2 depicts the fundamental structure of the Open-Calm model. A key feature of this architecture is the incorporation of the Lora Adapter and GPT-NeoX frameworks, which can enhance its language processing capabilities.

GPT-NeoX Model Architecture (Okgetheng and Takeuchi 2024 ).

In a recent study conducted by Okgetheng and Takeuchi ( 2024 ), they assessed the efficacy of Open-Calm language models in grading Japanese essays. The research utilized a dataset of approximately 300 essays, which were annotated by native Japanese educators. The findings of the study demonstrate the considerable potential of Open-Calm language models in automated Japanese essay scoring. Specifically, among the Open-Calm family, the Open-Calm Large model (referred to as OCLL) exhibited the highest performance. However, it is important to note that, as of the current date, the Open-Calm Large model does not offer public access to its server. Consequently, users are required to independently deploy and operate the environment for OCLL. In order to utilize OCLL, users must have a PC equipped with an NVIDIA GeForce RTX 3060 (8 or 12 GB VRAM).

In summary, while the potential of LLMs in automated scoring of nonnative Japanese essays has been demonstrated in two studies—BERT-driven AES (Hirao et al. 2020 ) and OCLL-based AES (Okgetheng and Takeuchi, 2024 )—the number of research efforts in this area remains limited.

Another significant challenge in applying LLMs to AES lies in prompt engineering and ensuring its reliability and effectiveness (Brown et al. 2020 ; Rae et al. 2021 ; Zhang et al. 2021 ). Various prompting strategies have been proposed, such as the zero-shot chain of thought (CoT) approach (Kojima et al. 2022 ), which involves manually crafting diverse and effective examples. However, manual efforts can lead to mistakes. To address this, Zhang et al. ( 2021 ) introduced an automatic CoT prompting method called Auto-CoT, which demonstrates matching or superior performance compared to the CoT paradigm. Another prompt framework is trees of thoughts, enabling a model to self-evaluate its progress at intermediate stages of problem-solving through deliberate reasoning (Yao et al. 2023 ).

Beyond linguistic studies, there has been a noticeable increase in the number of foreign workers in Japan and Japanese learners worldwide (Ministry of Health, Labor, and Welfare of Japan, 2022 ; Japan Foundation, 2021 ). However, existing assessment methods, such as the Japanese Language Proficiency Test (JLPT), J-CAT, and TTBJ Footnote 1 , primarily focus on reading, listening, vocabulary, and grammar skills, neglecting the evaluation of writing proficiency. As the number of workers and language learners continues to grow, there is a rising demand for an efficient AES system that can reduce costs and time for raters and be utilized for employment, examinations, and self-study purposes.

This study aims to explore the potential of LLM-based AES by comparing the effectiveness of five models: two LLMs (GPT Footnote 2 and BERT), one Japanese local LLM (OCLL), and two conventional machine learning-based methods (linguistic feature-based scoring tools - Jess and JWriter).

The research questions addressed in this study are as follows:

To what extent do the LLM-driven AES and linguistic feature-based AES, when used as automated tools to support human rating, accurately reflect test takers’ actual performance?

What influence does the prompt have on the accuracy and performance of LLM-based AES methods?

The subsequent sections of the manuscript cover the methodology, including the assessment measures for nonnative Japanese writing proficiency, criteria for prompts, and the dataset. The evaluation section focuses on the analysis of annotations and rating scores generated by LLM-driven and linguistic feature-based AES methods.

Methodology

The dataset utilized in this study was obtained from the International Corpus of Japanese as a Second Language (I-JAS) Footnote 3 . This corpus consisted of 1000 participants who represented 12 different first languages. For the study, the participants were given a story-writing task on a personal computer. They were required to write two stories based on the 4-panel illustrations titled “Picnic” and “The key” (see Appendix A). Background information for the participants was provided by the corpus, including their Japanese language proficiency levels assessed through two online tests: J-CAT and SPOT. These tests evaluated their reading, listening, vocabulary, and grammar abilities. The learners’ proficiency levels were categorized into six levels aligned with the Common European Framework of Reference for Languages (CEFR) and the Reference Framework for Japanese Language Education (RFJLE): A1, A2, B1, B2, C1, and C2. According to Lee et al. ( 2015 ), there is a high level of agreement (r = 0.86) between the J-CAT and SPOT assessments, indicating that the proficiency certifications provided by J-CAT are consistent with those of SPOT. However, it is important to note that the scores of J-CAT and SPOT do not have a one-to-one correspondence. In this study, the J-CAT scores were used as a benchmark to differentiate learners of different proficiency levels. A total of 1400 essays were utilized, representing the beginner (aligned with A1), A2, B1, B2, C1, and C2 levels based on the J-CAT scores. Table 1 provides information about the learners’ proficiency levels and their corresponding J-CAT and SPOT scores.

A dataset comprising a total of 1400 essays from the story writing tasks was collected. Among these, 714 essays were utilized to evaluate the reliability of the LLM-based AES method, while the remaining 686 essays were designated as development data to assess the LLM-based AES’s capability to distinguish participants with varying proficiency levels. The GPT 4 API was used in this study. A detailed explanation of the prompt-assessment criteria is provided in Section Prompt . All essays were sent to the model for measurement and scoring.

Measures of writing proficiency for nonnative Japanese

Japanese exhibits a morphologically agglutinative structure where morphemes are attached to the word stem to convey grammatical functions such as tense, aspect, voice, and honorifics, e.g. (5).

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

[eat (stem)-causative-passive voice-honorification-tense. past-question marker]

Japanese employs nine case particles to indicate grammatical functions: the nominative case particle が (ga), the accusative case particle を (o), the genitive case particle の (no), the dative case particle に (ni), the locative/instrumental case particle で (de), the ablative case particle から (kara), the directional case particle へ (e), and the comitative case particle と (to). The agglutinative nature of the language, combined with the case particle system, provides an efficient means of distinguishing between active and passive voice, either through morphemes or case particles, e.g. 食べる taberu “eat concusive . ” (active voice); 食べられる taberareru “eat concusive . ” (passive voice). In the active voice, “パン を 食べる” (pan o taberu) translates to “to eat bread”. On the other hand, in the passive voice, it becomes “パン が 食べられた” (pan ga taberareta), which means “(the) bread was eaten”. Additionally, it is important to note that different conjugations of the same lemma are considered as one type in order to ensure a comprehensive assessment of the language features. For example, e.g., 食べる taberu “eat concusive . ”; 食べている tabeteiru “eat progress .”; 食べた tabeta “eat past . ” as one type.

To incorporate these features, previous research (Suzuki, 1999 ; Watanabe et al. 1988 ; Ishioka, 2001 ; Ishioka and Kameda, 2006 ; Hirao et al. 2020 ) has identified complexity, fluency, and accuracy as crucial factors for evaluating writing quality. These criteria are assessed through various aspects, including lexical richness (lexical density, diversity, and sophistication), syntactic complexity, and cohesion (Kyle et al. 2021 ; Mizumoto and Eguchi, 2023 ; Ure, 1971 ; Halliday, 1985 ; Barkaoui and Hadidi, 2020 ; Zenker and Kyle, 2021 ; Kim et al. 2018 ; Lu, 2017 ; Ortega, 2015 ). Therefore, this study proposes five scoring categories: lexical richness, syntactic complexity, cohesion, content elaboration, and grammatical accuracy. A total of 16 measures were employed to capture these categories. The calculation process and specific details of these measures can be found in Table 2 .

T-unit, first introduced by Hunt ( 1966 ), is a measure used for evaluating speech and composition. It serves as an indicator of syntactic development and represents the shortest units into which a piece of discourse can be divided without leaving any sentence fragments. In the context of Japanese language assessment, Sakoda and Hosoi ( 2020 ) utilized T-unit as the basic unit to assess the accuracy and complexity of Japanese learners’ speaking and storytelling. The calculation of T-units in Japanese follows the following principles:

A single main clause constitutes 1 T-unit, regardless of the presence or absence of dependent clauses, e.g. (6).

ケンとマリはピクニックに行きました (main clause): 1 T-unit.

If a sentence contains a main clause along with subclauses, each subclause is considered part of the same T-unit, e.g. (7).

天気が良かった の で (subclause)、ケンとマリはピクニックに行きました (main clause): 1 T-unit.

In the case of coordinate clauses, where multiple clauses are connected, each coordinated clause is counted separately. Thus, a sentence with coordinate clauses may have 2 T-units or more, e.g. (8).

ケンは地図で場所を探して (coordinate clause)、マリはサンドイッチを作りました (coordinate clause): 2 T-units.

Lexical diversity refers to the range of words used within a text (Engber, 1995 ; Kyle et al. 2021 ) and is considered a useful measure of the breadth of vocabulary in L n production (Jarvis, 2013a , 2013b ).

The type/token ratio (TTR) is widely recognized as a straightforward measure for calculating lexical diversity and has been employed in numerous studies. These studies have demonstrated a strong correlation between TTR and other methods of measuring lexical diversity (e.g., Bentz et al. 2016 ; Čech and Miroslav, 2018 ; Çöltekin and Taraka, 2018 ). TTR is computed by considering both the number of unique words (types) and the total number of words (tokens) in a given text. Given that the length of learners’ writing texts can vary, this study employs the moving average type-token ratio (MATTR) to mitigate the influence of text length. MATTR is calculated using a 50-word moving window. Initially, a TTR is determined for words 1–50 in an essay, followed by words 2–51, 3–52, and so on until the end of the essay is reached (Díez-Ortega and Kyle, 2023 ). The final MATTR scores were obtained by averaging the TTR scores for all 50-word windows. The following formula was employed to derive MATTR:

\({\rm{MATTR}}({\rm{W}})=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{N}}-{\rm{W}}+1}{{\rm{F}}}_{{\rm{i}}}}{{\rm{W}}({\rm{N}}-{\rm{W}}+1)}\)

Here, N refers to the number of tokens in the corpus. W is the randomly selected token size (W < N). \({F}_{i}\) is the number of types in each window. The \({\rm{MATTR}}({\rm{W}})\) is the mean of a series of type-token ratios (TTRs) based on the word form for all windows. It is expected that individuals with higher language proficiency will produce texts with greater lexical diversity, as indicated by higher MATTR scores.

Lexical density was captured by the ratio of the number of lexical words to the total number of words (Lu, 2012 ). Lexical sophistication refers to the utilization of advanced vocabulary, often evaluated through word frequency indices (Crossley et al. 2013 ; Haberman, 2008 ; Kyle and Crossley, 2015 ; Laufer and Nation, 1995 ; Lu, 2012 ; Read, 2000 ). In line of writing, lexical sophistication can be interpreted as vocabulary breadth, which entails the appropriate usage of vocabulary items across various lexicon-grammatical contexts and registers (Garner et al. 2019 ; Kim et al. 2018 ; Kyle et al. 2018 ). In Japanese specifically, words are considered lexically sophisticated if they are not included in the “Japanese Education Vocabulary List Ver 1.0”. Footnote 4 Consequently, lexical sophistication was calculated by determining the number of sophisticated word types relative to the total number of words per essay. Furthermore, it has been suggested that, in Japanese writing, sentences should ideally have a length of no more than 40 to 50 characters, as this promotes readability. Therefore, the median and maximum sentence length can be considered as useful indices for assessment (Ishioka and Kameda, 2006 ).

Syntactic complexity was assessed based on several measures, including the mean length of clauses, verb phrases per T-unit, clauses per T-unit, dependent clauses per T-unit, complex nominals per clause, adverbial clauses per clause, coordinate phrases per clause, and mean dependency distance (MDD). The MDD reflects the distance between the governor and dependent positions in a sentence. A larger dependency distance indicates a higher cognitive load and greater complexity in syntactic processing (Liu, 2008 ; Liu et al. 2017 ). The MDD has been established as an efficient metric for measuring syntactic complexity (Jiang, Quyang, and Liu, 2019 ; Li and Yan, 2021 ). To calculate the MDD, the position numbers of the governor and dependent are subtracted, assuming that words in a sentence are assigned in a linear order, such as W1 … Wi … Wn. In any dependency relationship between words Wa and Wb, Wa is the governor and Wb is the dependent. The MDD of the entire sentence was obtained by taking the absolute value of governor – dependent:

MDD = \(\frac{1}{n}{\sum }_{i=1}^{n}|{\rm{D}}{{\rm{D}}}_{i}|\)

In this formula, \(n\) represents the number of words in the sentence, and \({DD}i\) is the dependency distance of the \({i}^{{th}}\) dependency relationship of a sentence. Building on this, the annotation of sentence ‘Mary-ga-John-ni-keshigomu-o-watashita was [Mary- top -John- dat -eraser- acc -give- past] ’. The sentence’s MDD would be 2. Table 3 provides the CSV file as a prompt for GPT 4.

Cohesion (semantic similarity) and content elaboration aim to capture the ideas presented in test taker’s essays. Cohesion was assessed using three measures: Synonym overlap/paragraph (topic), Synonym overlap/paragraph (keywords), and word2vec cosine similarity. Content elaboration and development were measured as the number of metadiscourse markers (type)/number of words. To capture content closely, this study proposed a novel-distance based representation, by encoding the cosine distance between the essay (by learner) and essay task’s (topic and keyword) i -vectors. The learner’s essay is decoded into a word sequence, and aligned to the essay task’ topic and keyword for log-likelihood measurement. The cosine distance reveals the content elaboration score in the leaners’ essay. The mathematical equation of cosine similarity between target-reference vectors is shown in (11), assuming there are i essays and ( L i , …. L n ) and ( N i , …. N n ) are the vectors representing the learner and task’s topic and keyword respectively. The content elaboration distance between L i and N i was calculated as follows:

\(\cos \left(\theta \right)=\frac{{\rm{L}}\,\cdot\, {\rm{N}}}{\left|{\rm{L}}\right|{\rm{|N|}}}=\frac{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}{N}_{i}}{\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}^{2}}\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{N}_{i}^{2}}}\)

A high similarity value indicates a low difference between the two recognition outcomes, which in turn suggests a high level of proficiency in content elaboration.

To evaluate the effectiveness of the proposed measures in distinguishing different proficiency levels among nonnative Japanese speakers’ writing, we conducted a multi-faceted Rasch measurement analysis (Linacre, 1994 ). This approach applies measurement models to thoroughly analyze various factors that can influence test outcomes, including test takers’ proficiency, item difficulty, and rater severity, among others. The underlying principles and functionality of multi-faceted Rasch measurement are illustrated in (12).

\(\log \left(\frac{{P}_{{nijk}}}{{P}_{{nij}(k-1)}}\right)={B}_{n}-{D}_{i}-{C}_{j}-{F}_{k}\)

(12) defines the logarithmic transformation of the probability ratio ( P nijk /P nij(k-1) )) as a function of multiple parameters. Here, n represents the test taker, i denotes a writing proficiency measure, j corresponds to the human rater, and k represents the proficiency score. The parameter B n signifies the proficiency level of test taker n (where n ranges from 1 to N). D j represents the difficulty parameter of test item i (where i ranges from 1 to L), while C j represents the severity of rater j (where j ranges from 1 to J). Additionally, F k represents the step difficulty for a test taker to move from score ‘k-1’ to k . P nijk refers to the probability of rater j assigning score k to test taker n for test item i . P nij(k-1) represents the likelihood of test taker n being assigned score ‘k-1’ by rater j for test item i . Each facet within the test is treated as an independent parameter and estimated within the same reference framework. To evaluate the consistency of scores obtained through both human and computer analysis, we utilized the Infit mean-square statistic. This statistic is a chi-square measure divided by the degrees of freedom and is weighted with information. It demonstrates higher sensitivity to unexpected patterns in responses to items near a person’s proficiency level (Linacre, 2002 ). Fit statistics are assessed based on predefined thresholds for acceptable fit. For the Infit MNSQ, which has a mean of 1.00, different thresholds have been suggested. Some propose stricter thresholds ranging from 0.7 to 1.3 (Bond et al. 2021 ), while others suggest more lenient thresholds ranging from 0.5 to 1.5 (Eckes, 2009 ). In this study, we adopted the criterion of 0.70–1.30 for the Infit MNSQ.

Moving forward, we can now proceed to assess the effectiveness of the 16 proposed measures based on five criteria for accurately distinguishing various levels of writing proficiency among non-native Japanese speakers. To conduct this evaluation, we utilized the development dataset from the I-JAS corpus, as described in Section Dataset . Table 4 provides a measurement report that presents the performance details of the 14 metrics under consideration. The measure separation was found to be 4.02, indicating a clear differentiation among the measures. The reliability index for the measure separation was 0.891, suggesting consistency in the measurement. Similarly, the person separation reliability index was 0.802, indicating the accuracy of the assessment in distinguishing between individuals. All 16 measures demonstrated Infit mean squares within a reasonable range, ranging from 0.76 to 1.28. The Synonym overlap/paragraph (topic) measure exhibited a relatively high outfit mean square of 1.46, although the Infit mean square falls within an acceptable range. The standard error for the measures ranged from 0.13 to 0.28, indicating the precision of the estimates.

Table 5 further illustrated the weights assigned to different linguistic measures for score prediction, with higher weights indicating stronger correlations between those measures and higher scores. Specifically, the following measures exhibited higher weights compared to others: moving average type token ratio per essay has a weight of 0.0391. Mean dependency distance had a weight of 0.0388. Mean length of clause, calculated by dividing the number of words by the number of clauses, had a weight of 0.0374. Complex nominals per T-unit, calculated by dividing the number of complex nominals by the number of T-units, had a weight of 0.0379. Coordinate phrases rate, calculated by dividing the number of coordinate phrases by the number of clauses, had a weight of 0.0325. Grammatical error rate, representing the number of errors per essay, had a weight of 0.0322.

Criteria (output indicator)

The criteria used to evaluate the writing ability in this study were based on CEFR, which follows a six-point scale ranging from A1 to C2. To assess the quality of Japanese writing, the scoring criteria from Table 6 were utilized. These criteria were derived from the IELTS writing standards and served as assessment guidelines and prompts for the written output.

A prompt is a question or detailed instruction that is provided to the model to obtain a proper response. After several pilot experiments, we decided to provide the measures (Section Measures of writing proficiency for nonnative Japanese ) as the input prompt and use the criteria (Section Criteria (output indicator) ) as the output indicator. Regarding the prompt language, considering that the LLM was tasked with rating Japanese essays, would prompt in Japanese works better Footnote 5 ? We conducted experiments comparing the performance of GPT-4 using both English and Japanese prompts. Additionally, we utilized the Japanese local model OCLL with Japanese prompts. Multiple trials were conducted using the same sample. Regardless of the prompt language used, we consistently obtained the same grading results with GPT-4, which assigned a grade of B1 to the writing sample. This suggested that GPT-4 is reliable and capable of producing consistent ratings regardless of the prompt language. On the other hand, when we used Japanese prompts with the Japanese local model “OCLL”, we encountered inconsistent grading results. Out of 10 attempts with OCLL, only 6 yielded consistent grading results (B1), while the remaining 4 showed different outcomes, including A1 and B2 grades. These findings indicated that the language of the prompt was not the determining factor for reliable AES. Instead, the size of the training data and the model parameters played crucial roles in achieving consistent and reliable AES results for the language model.

The following is the utilized prompt, which details all measures and requires the LLM to score the essays using holistic and trait scores.

Please evaluate Japanese essays written by Japanese learners and assign a score to each essay on a six-point scale, ranging from A1, A2, B1, B2, C1 to C2. Additionally, please provide trait scores and display the calculation process for each trait score. The scoring should be based on the following criteria:

Moving average type-token ratio.

Number of lexical words (token) divided by the total number of words per essay.

Number of sophisticated word types divided by the total number of words per essay.

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

Number of metadiscourse markers (types) divided by the total number of words.

Number of errors per essay.

Japanese essay text

出かける前に二人が地図を見ている間に、サンドイッチを入れたバスケットに犬が入ってしまいました。それに気づかずに二人は楽しそうに出かけて行きました。やがて突然犬がバスケットから飛び出し、二人は驚きました。バスケット の 中を見ると、食べ物はすべて犬に食べられていて、二人は困ってしまいました。(ID_JJJ01_SW1)

The score of the example above was B1. Figure 3 provides an example of holistic and trait scores provided by GPT-4 (with a prompt indicating all measures) via Bing Footnote 6 .

Example of GPT-4 AES and feedback (with a prompt indicating all measures).

Statistical analysis

The aim of this study is to investigate the potential use of LLM for nonnative Japanese AES. It seeks to compare the scoring outcomes obtained from feature-based AES tools, which rely on conventional machine learning technology (i.e. Jess, JWriter), with those generated by AI-driven AES tools utilizing deep learning technology (BERT, GPT, OCLL). To assess the reliability of a computer-assisted annotation tool, the study initially established human-human agreement as the benchmark measure. Subsequently, the performance of the LLM-based method was evaluated by comparing it to human-human agreement.

To assess annotation agreement, the study employed standard measures such as precision, recall, and F-score (Brants 2000 ; Lu 2010 ), along with the quadratically weighted kappa (QWK) to evaluate the consistency and agreement in the annotation process. Assume A and B represent human annotators. When comparing the annotations of the two annotators, the following results are obtained. The evaluation of precision, recall, and F-score metrics was illustrated in equations (13) to (15).

\({\rm{Recall}}(A,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,A}\)

\({\rm{Precision}}(A,\,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,B}\)

The F-score is the harmonic mean of recall and precision:

\({\rm{F}}-{\rm{score}}=\frac{2* ({\rm{Precision}}* {\rm{Recall}})}{{\rm{Precision}}+{\rm{Recall}}}\)

The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.

In accordance with Taghipour and Ng ( 2016 ), the calculation of QWK involves two steps:

Step 1: Construct a weight matrix W as follows:

\({W}_{{ij}}=\frac{{(i-j)}^{2}}{{(N-1)}^{2}}\)

i represents the annotation made by the tool, while j represents the annotation made by a human rater. N denotes the total number of possible annotations. Matrix O is subsequently computed, where O_( i, j ) represents the count of data annotated by the tool ( i ) and the human annotator ( j ). On the other hand, E refers to the expected count matrix, which undergoes normalization to ensure that the sum of elements in E matches the sum of elements in O.

Step 2: With matrices O and E, the QWK is obtained as follows:

K = 1- \(\frac{\sum i,j{W}_{i,j}\,{O}_{i,j}}{\sum i,j{W}_{i,j}\,{E}_{i,j}}\)

The value of the quadratic weighted kappa increases as the level of agreement improves. Further, to assess the accuracy of LLM scoring, the proportional reductive mean square error (PRMSE) was employed. The PRMSE approach takes into account the variability observed in human ratings to estimate the rater error, which is then subtracted from the variance of the human labels. This calculation provides an overall measure of agreement between the automated scores and true scores (Haberman et al. 2015 ; Loukina et al. 2020 ; Taghipour and Ng, 2016 ). The computation of PRMSE involves the following steps:

Step 1: Calculate the mean squared errors (MSEs) for the scoring outcomes of the computer-assisted tool (MSE tool) and the human scoring outcomes (MSE human).

Step 2: Determine the PRMSE by comparing the MSE of the computer-assisted tool (MSE tool) with the MSE from human raters (MSE human), using the following formula:

\({\rm{PRMSE}}=1-\frac{({\rm{MSE}}\,{\rm{tool}})\,}{({\rm{MSE}}\,{\rm{human}})\,}=1-\,\frac{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-{\hat{{\rm{y}}}}_{{\rm{i}}})}^{2}}{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-\hat{{\rm{y}}})}^{2}}\)

In the numerator, ŷi represents the scoring outcome predicted by a specific LLM-driven AES system for a given sample. The term y i − ŷ i represents the difference between this predicted outcome and the mean value of all LLM-driven AES systems’ scoring outcomes. It quantifies the deviation of the specific LLM-driven AES system’s prediction from the average prediction of all LLM-driven AES systems. In the denominator, y i − ŷ represents the difference between the scoring outcome provided by a specific human rater for a given sample and the mean value of all human raters’ scoring outcomes. It measures the discrepancy between the specific human rater’s score and the average score given by all human raters. The PRMSE is then calculated by subtracting the ratio of the MSE tool to the MSE human from 1. PRMSE falls within the range of 0 to 1, with larger values indicating reduced errors in LLM’s scoring compared to those of human raters. In other words, a higher PRMSE implies that LLM’s scoring demonstrates greater accuracy in predicting the true scores (Loukina et al. 2020 ). The interpretation of kappa values, ranging from 0 to 1, is based on the work of Landis and Koch ( 1977 ). Specifically, the following categories are assigned to different ranges of kappa values: −1 indicates complete inconsistency, 0 indicates random agreement, 0.0 ~ 0.20 indicates extremely low level of agreement (slight), 0.21 ~ 0.40 indicates moderate level of agreement (fair), 0.41 ~ 0.60 indicates medium level of agreement (moderate), 0.61 ~ 0.80 indicates high level of agreement (substantial), 0.81 ~ 1 indicates almost perfect level of agreement. All statistical analyses were executed using Python script.

Results and discussion

Annotation reliability of the llm.

This section focuses on assessing the reliability of the LLM’s annotation and scoring capabilities. To evaluate the reliability, several tests were conducted simultaneously, aiming to achieve the following objectives:

Assess the LLM’s ability to differentiate between test takers with varying levels of oral proficiency.

Determine the level of agreement between the annotations and scoring performed by the LLM and those done by human raters.

The evaluation of the results encompassed several metrics, including: precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement.

Inter-annotator agreement (human–human annotator agreement)

We started with an agreement test of the two human annotators. Two trained annotators were recruited to determine the writing task data measures. A total of 714 scripts, as the test data, was utilized. Each analysis lasted 300–360 min. Inter-annotator agreement was evaluated using the standard measures of precision, recall, and F-score and QWK. Table 7 presents the inter-annotator agreement for the various indicators. As shown, the inter-annotator agreement was fairly high, with F-scores ranging from 1.0 for sentence and word number to 0.666 for grammatical errors.

The findings from the QWK analysis provided further confirmation of the inter-annotator agreement. The QWK values covered a range from 0.950 ( p  = 0.000) for sentence and word number to 0.695 for synonym overlap number (keyword) and grammatical errors ( p  = 0.001).

Agreement of annotation outcomes between human and LLM

To evaluate the consistency between human annotators and LLM annotators (BERT, GPT, OCLL) across the indices, the same test was conducted. The results of the inter-annotator agreement (F-score) between LLM and human annotation are provided in Appendix B-D. The F-scores ranged from 0.706 for Grammatical error # for OCLL-human to a perfect 1.000 for GPT-human, for sentences, clauses, T-units, and words. These findings were further supported by the QWK analysis, which showed agreement levels ranging from 0.807 ( p  = 0.001) for metadiscourse markers for OCLL-human to 0.962 for words ( p  = 0.000) for GPT-human. The findings demonstrated that the LLM annotation achieved a significant level of accuracy in identifying measurement units and counts.

Reliability of LLM-driven AES’s scoring and discriminating proficiency levels

This section examines the reliability of the LLM-driven AES scoring through a comparison of the scoring outcomes produced by human raters and the LLM ( Reliability of LLM-driven AES scoring ). It also assesses the effectiveness of the LLM-based AES system in differentiating participants with varying proficiency levels ( Reliability of LLM-driven AES discriminating proficiency levels ).

Reliability of LLM-driven AES scoring

Table 8 summarizes the QWK coefficient analysis between the scores computed by the human raters and the GPT-4 for the individual essays from I-JAS Footnote 7 . As shown, the QWK of all measures ranged from k  = 0.819 for lexical density (number of lexical words (tokens)/number of words per essay) to k  = 0.644 for word2vec cosine similarity. Table 9 further presents the Pearson correlations between the 16 writing proficiency measures scored by human raters and GPT 4 for the individual essays. The correlations ranged from 0.672 for syntactic complexity to 0.734 for grammatical accuracy. The correlations between the writing proficiency scores assigned by human raters and the BERT-based AES system were found to range from 0.661 for syntactic complexity to 0.713 for grammatical accuracy. The correlations between the writing proficiency scores given by human raters and the OCLL-based AES system ranged from 0.654 for cohesion to 0.721 for grammatical accuracy. These findings indicated an alignment between the assessments made by human raters and both the BERT-based and OCLL-based AES systems in terms of various aspects of writing proficiency.

Reliability of LLM-driven AES discriminating proficiency levels

After validating the reliability of the LLM’s annotation and scoring, the subsequent objective was to evaluate its ability to distinguish between various proficiency levels. For this analysis, a dataset of 686 individual essays was utilized. Table 10 presents a sample of the results, summarizing the means, standard deviations, and the outcomes of the one-way ANOVAs based on the measures assessed by the GPT-4 model. A post hoc multiple comparison test, specifically the Bonferroni test, was conducted to identify any potential differences between pairs of levels.

As the results reveal, seven measures presented linear upward or downward progress across the three proficiency levels. These were marked in bold in Table 10 and comprise one measure of lexical richness, i.e. MATTR (lexical diversity); four measures of syntactic complexity, i.e. MDD (mean dependency distance), MLC (mean length of clause), CNT (complex nominals per T-unit), CPC (coordinate phrases rate); one cohesion measure, i.e. word2vec cosine similarity and GER (grammatical error rate). Regarding the ability of the sixteen measures to distinguish adjacent proficiency levels, the Bonferroni tests indicated that statistically significant differences exist between the primary level and the intermediate level for MLC and GER. One measure of lexical richness, namely LD, along with three measures of syntactic complexity (VPT, CT, DCT, ACC), two measures of cohesion (SOPT, SOPK), and one measure of content elaboration (IMM), exhibited statistically significant differences between proficiency levels. However, these differences did not demonstrate a linear progression between adjacent proficiency levels. No significant difference was observed in lexical sophistication between proficiency levels.

To summarize, our study aimed to evaluate the reliability and differentiation capabilities of the LLM-driven AES method. For the first objective, we assessed the LLM’s ability to differentiate between test takers with varying levels of oral proficiency using precision, recall, F-Score, and quadratically-weighted kappa. Regarding the second objective, we compared the scoring outcomes generated by human raters and the LLM to determine the level of agreement. We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency measures for the individual essays. The results confirmed the feasibility of using the LLM for annotation and scoring in AES for nonnative Japanese. As a result, Research Question 1 has been addressed.

Comparison of BERT-, GPT-, OCLL-based AES, and linguistic-feature-based computation methods

This section aims to compare the effectiveness of five AES methods for nonnative Japanese writing, i.e. LLM-driven approaches utilizing BERT, GPT, and OCLL, linguistic feature-based approaches using Jess and JWriter. The comparison was conducted by comparing the ratings obtained from each approach with human ratings. All ratings were derived from the dataset introduced in Dataset . To facilitate the comparison, the agreement between the automated methods and human ratings was assessed using QWK and PRMSE. The performance of each approach was summarized in Table 11 .

The QWK coefficient values indicate that LLMs (GPT, BERT, OCLL) and human rating outcomes demonstrated higher agreement compared to feature-based AES methods (Jess and JWriter) in assessing writing proficiency criteria, including lexical richness, syntactic complexity, content, and grammatical accuracy. Among the LLMs, the GPT-4 driven AES and human rating outcomes showed the highest agreement in all criteria, except for syntactic complexity. The PRMSE values suggest that the GPT-based method outperformed linguistic feature-based methods and other LLM-based approaches. Moreover, an interesting finding emerged during the study: the agreement coefficient between GPT-4 and human scoring was even higher than the agreement between different human raters themselves. This discovery highlights the advantage of GPT-based AES over human rating. Ratings involve a series of processes, including reading the learners’ writing, evaluating the content and language, and assigning scores. Within this chain of processes, various biases can be introduced, stemming from factors such as rater biases, test design, and rating scales. These biases can impact the consistency and objectivity of human ratings. GPT-based AES may benefit from its ability to apply consistent and objective evaluation criteria. By prompting the GPT model with detailed writing scoring rubrics and linguistic features, potential biases in human ratings can be mitigated. The model follows a predefined set of guidelines and does not possess the same subjective biases that human raters may exhibit. This standardization in the evaluation process contributes to the higher agreement observed between GPT-4 and human scoring. Section Prompt strategy of the study delves further into the role of prompts in the application of LLMs to AES. It explores how the choice and implementation of prompts can impact the performance and reliability of LLM-based AES methods. Furthermore, it is important to acknowledge the strengths of the local model, i.e. the Japanese local model OCLL, which excels in processing certain idiomatic expressions. Nevertheless, our analysis indicated that GPT-4 surpasses local models in AES. This superior performance can be attributed to the larger parameter size of GPT-4, estimated to be between 500 billion and 1 trillion, which exceeds the sizes of both BERT and the local model OCLL.

Prompt strategy

In the context of prompt strategy, Mizumoto and Eguchi ( 2023 ) conducted a study where they applied the GPT-3 model to automatically score English essays in the TOEFL test. They found that the accuracy of the GPT model alone was moderate to fair. However, when they incorporated linguistic measures such as cohesion, syntactic complexity, and lexical features alongside the GPT model, the accuracy significantly improved. This highlights the importance of prompt engineering and providing the model with specific instructions to enhance its performance. In this study, a similar approach was taken to optimize the performance of LLMs. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. Model 1 was used as the baseline, representing GPT-4 without any additional prompting. Model 2, on the other hand, involved GPT-4 prompted with 16 measures that included scoring criteria, efficient linguistic features for writing assessment, and detailed measurement units and calculation formulas. The remaining models (Models 3 to 18) utilized GPT-4 prompted with individual measures. The performance of these 18 different models was assessed using the output indicators described in Section Criteria (output indicator) . By comparing the performances of these models, the study aimed to understand the impact of prompt engineering on the accuracy and effectiveness of GPT-4 in AES tasks.

Based on the PRMSE scores presented in Fig. 4 , it was observed that Model 1, representing GPT-4 without any additional prompting, achieved a fair level of performance. However, Model 2, which utilized GPT-4 prompted with all measures, outperformed all other models in terms of PRMSE score, achieving a score of 0.681. These results indicate that the inclusion of specific measures and prompts significantly enhanced the performance of GPT-4 in AES. Among the measures, syntactic complexity was found to play a particularly significant role in improving the accuracy of GPT-4 in assessing writing quality. Following that, lexical diversity emerged as another important factor contributing to the model’s effectiveness. The study suggests that a well-prompted GPT-4 can serve as a valuable tool to support human assessors in evaluating writing quality. By utilizing GPT-4 as an automated scoring tool, the evaluation biases associated with human raters can be minimized. This has the potential to empower teachers by allowing them to focus on designing writing tasks and guiding writing strategies, while leveraging the capabilities of GPT-4 for efficient and reliable scoring.

PRMSE scores of the 18 AES models.

This study aimed to investigate two main research questions: the feasibility of utilizing LLMs for AES and the impact of prompt engineering on the application of LLMs in AES.

To address the first objective, the study compared the effectiveness of five different models: GPT, BERT, the Japanese local LLM (OCLL), and two conventional machine learning-based AES tools (Jess and JWriter). The PRMSE values indicated that the GPT-4-based method outperformed other LLMs (BERT, OCLL) and linguistic feature-based computational methods (Jess and JWriter) across various writing proficiency criteria. Furthermore, the agreement coefficient between GPT-4 and human scoring surpassed the agreement among human raters themselves, highlighting the potential of using the GPT-4 tool to enhance AES by reducing biases and subjectivity, saving time, labor, and cost, and providing valuable feedback for self-study. Regarding the second goal, the role of prompt design was investigated by comparing 18 models, including a baseline model, a model prompted with all measures, and 16 models prompted with one measure at a time. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. The PRMSE scores of the models showed that GPT-4 prompted with all measures achieved the best performance, surpassing the baseline and other models.

In conclusion, this study has demonstrated the potential of LLMs in supporting human rating in assessments. By incorporating automation, we can save time and resources while reducing biases and subjectivity inherent in human rating processes. Automated language assessments offer the advantage of accessibility, providing equal opportunities and economic feasibility for individuals who lack access to traditional assessment centers or necessary resources. LLM-based language assessments provide valuable feedback and support to learners, aiding in the enhancement of their language proficiency and the achievement of their goals. This personalized feedback can cater to individual learner needs, facilitating a more tailored and effective language-learning experience.

There are three important areas that merit further exploration. First, prompt engineering requires attention to ensure optimal performance of LLM-based AES across different language types. This study revealed that GPT-4, when prompted with all measures, outperformed models prompted with fewer measures. Therefore, investigating and refining prompt strategies can enhance the effectiveness of LLMs in automated language assessments. Second, it is crucial to explore the application of LLMs in second-language assessment and learning for oral proficiency, as well as their potential in under-resourced languages. Recent advancements in self-supervised machine learning techniques have significantly improved automatic speech recognition (ASR) systems, opening up new possibilities for creating reliable ASR systems, particularly for under-resourced languages with limited data. However, challenges persist in the field of ASR. First, ASR assumes correct word pronunciation for automatic pronunciation evaluation, which proves challenging for learners in the early stages of language acquisition due to diverse accents influenced by their native languages. Accurately segmenting short words becomes problematic in such cases. Second, developing precise audio-text transcriptions for languages with non-native accented speech poses a formidable task. Last, assessing oral proficiency levels involves capturing various linguistic features, including fluency, pronunciation, accuracy, and complexity, which are not easily captured by current NLP technology.

Data availability

The dataset utilized was obtained from the International Corpus of Japanese as a Second Language (I-JAS). The data URLs: [ https://www2.ninjal.ac.jp/jll/lsaj/ihome2.html ].

J-CAT and TTBJ are two computerized adaptive tests used to assess Japanese language proficiency.

SPOT is a specific component of the TTBJ test.

J-CAT: https://www.j-cat2.org/html/ja/pages/interpret.html

SPOT: https://ttbj.cegloc.tsukuba.ac.jp/p1.html#SPOT .

The study utilized a prompt-based GPT-4 model, developed by OpenAI, which has an impressive architecture with 1.8 trillion parameters across 120 layers. GPT-4 was trained on a vast dataset of 13 trillion tokens, using two stages: initial training on internet text datasets to predict the next token, and subsequent fine-tuning through reinforcement learning from human feedback.

https://www2.ninjal.ac.jp/jll/lsaj/ihome2-en.html .

http://jhlee.sakura.ne.jp/JEV/ by Japanese Learning Dictionary Support Group 2015.

We express our sincere gratitude to the reviewer for bringing this matter to our attention.

On February 7, 2023, Microsoft began rolling out a major overhaul to Bing that included a new chatbot feature based on OpenAI’s GPT-4 (Bing.com).

Appendix E-F present the analysis results of the QWK coefficient between the scores computed by the human raters and the BERT, OCLL models.

Attali Y, Burstein J (2006) Automated essay scoring with e-rater® V.2. J. Technol., Learn. Assess., 4

Barkaoui K, Hadidi A (2020) Assessing Change in English Second Language Writing Performance (1st ed.). Routledge, New York. https://doi.org/10.4324/9781003092346

Bentz C, Tatyana R, Koplenig A, Tanja S (2016) A comparison between morphological complexity. measures: Typological data vs. language corpora. In Proceedings of the workshop on computational linguistics for linguistic complexity (CL4LC), 142–153. Osaka, Japan: The COLING 2016 Organizing Committee

Bond TG, Yan Z, Heene M (2021) Applying the Rasch model: Fundamental measurement in the human sciences (4th ed). Routledge

Brants T (2000) Inter-annotator agreement for a German newspaper corpus. Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00), Athens, Greece, 31 May-2 June, European Language Resources Association

Brown TB, Mann B, Ryder N, et al. (2020) Language models are few-shot learners. Advances in Neural Information Processing Systems, Online, 6–12 December, Curran Associates, Inc., Red Hook, NY

Burstein J (2003) The E-rater scoring engine: Automated essay scoring with natural language processing. In Shermis MD and Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Čech R, Miroslav K (2018) Morphological richness of text. In Masako F, Václav C (ed) Taming the corpus: From inflection and lexis to interpretation, 63–77. Cham, Switzerland: Springer Nature

Çöltekin Ç, Taraka, R (2018) Exploiting Universal Dependencies treebanks for measuring morphosyntactic complexity. In Aleksandrs B, Christian B (ed), Proceedings of first workshop on measuring language complexity, 1–7. Torun, Poland

Crossley SA, Cobb T, McNamara DS (2013) Comparing count-based and band-based indices of word frequency: Implications for active vocabulary research and pedagogical applications. System 41:965–981. https://doi.org/10.1016/j.system.2013.08.002

Article   Google Scholar  

Crossley SA, McNamara DS (2016) Say more and be more coherent: How text elaboration and cohesion can increase writing quality. J. Writ. Res. 7:351–370

CyberAgent Inc (2023) Open-Calm series of Japanese language models. Retrieved from: https://www.cyberagent.co.jp/news/detail/id=28817

Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, Minnesota, 2–7 June, pp. 4171–4186. Association for Computational Linguistics

Diez-Ortega M, Kyle K (2023) Measuring the development of lexical richness of L2 Spanish: a longitudinal learner corpus study. Studies in Second Language Acquisition 1-31

Eckes T (2009) On common ground? How raters perceive scoring criteria in oral proficiency testing. In Brown A, Hill K (ed) Language testing and evaluation 13: Tasks and criteria in performance assessment (pp. 43–73). Peter Lang Publishing

Elliot S (2003) IntelliMetric: from here to validity. In: Shermis MD, Burstein JC (ed) Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Google Scholar  

Engber CA (1995) The relationship of lexical proficiency to the quality of ESL compositions. J. Second Lang. Writ. 4:139–155

Garner J, Crossley SA, Kyle K (2019) N-gram measures and L2 writing proficiency. System 80:176–187. https://doi.org/10.1016/j.system.2018.12.001

Haberman SJ (2008) When can subscores have value? J. Educat. Behav. Stat., 33:204–229

Haberman SJ, Yao L, Sinharay S (2015) Prediction of true test scores from observed item scores and ancillary data. Brit. J. Math. Stat. Psychol. 68:363–385

Halliday MAK (1985) Spoken and Written Language. Deakin University Press, Melbourne, Australia

Hirao R, Arai M, Shimanaka H et al. (2020) Automated essay scoring system for nonnative Japanese learners. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pp. 1250–1257. European Language Resources Association

Hunt KW (1966) Recent Measures in Syntactic Development. Elementary English, 43(7), 732–739. http://www.jstor.org/stable/41386067

Ishioka T (2001) About e-rater, a computer-based automatic scoring system for essays [Konpyūta ni yoru essei no jidō saiten shisutemu e − rater ni tsuite]. University Entrance Examination. Forum [Daigaku nyūshi fōramu] 24:71–76

Hochreiter S, Schmidhuber J (1997) Long short- term memory. Neural Comput. 9(8):1735–1780

Article   CAS   PubMed   Google Scholar  

Ishioka T, Kameda M (2006) Automated Japanese essay scoring system based on articles written by experts. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia, 17–18 July 2006, pp. 233-240. Association for Computational Linguistics, USA

Japan Foundation (2021) Retrieved from: https://www.jpf.gp.jp/j/project/japanese/survey/result/dl/survey2021/all.pdf

Jarvis S (2013a) Defining and measuring lexical diversity. In Jarvis S, Daller M (ed) Vocabulary knowledge: Human ratings and automated measures (Vol. 47, pp. 13–44). John Benjamins. https://doi.org/10.1075/sibil.47.03ch1

Jarvis S (2013b) Capturing the diversity in lexical diversity. Lang. Learn. 63:87–106. https://doi.org/10.1111/j.1467-9922.2012.00739.x

Jiang J, Quyang J, Liu H (2019) Interlanguage: A perspective of quantitative linguistic typology. Lang. Sci. 74:85–97

Kim M, Crossley SA, Kyle K (2018) Lexical sophistication as a multidimensional phenomenon: Relations to second language lexical proficiency, development, and writing quality. Mod. Lang. J. 102(1):120–141. https://doi.org/10.1111/modl.12447

Kojima T, Gu S, Reid M et al. (2022) Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, New Orleans, LA, 29 November-1 December, Curran Associates, Inc., Red Hook, NY

Kyle K, Crossley SA (2015) Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Q 49:757–786

Kyle K, Crossley SA, Berger CM (2018) The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behav. Res. Methods 50:1030–1046. https://doi.org/10.3758/s13428-017-0924-4

Article   PubMed   Google Scholar  

Kyle K, Crossley SA, Jarvis S (2021) Assessing the validity of lexical diversity using direct judgements. Lang. Assess. Q. 18:154–170. https://doi.org/10.1080/15434303.2020.1844205

Landauer TK, Laham D, Foltz PW (2003) Automated essay scoring and annotation of essays with the Intelligent Essay Assessor. In Shermis MD, Burstein JC (ed), Automated Essay Scoring: A Cross-Disciplinary Perspective. Lawrence Erlbaum Associates, Mahwah, NJ

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 159–174

Laufer B, Nation P (1995) Vocabulary size and use: Lexical richness in L2 written production. Appl. Linguist. 16:307–322. https://doi.org/10.1093/applin/16.3.307

Lee J, Hasebe Y (2017) jWriter Learner Text Evaluator, URL: https://jreadability.net/jwriter/

Lee J, Kobayashi N, Sakai T, Sakota K (2015) A Comparison of SPOT and J-CAT Based on Test Analysis [Tesuto bunseki ni motozuku ‘SPOT’ to ‘J-CAT’ no hikaku]. Research on the Acquisition of Second Language Japanese [Dainigengo to shite no nihongo no shūtoku kenkyū] (18) 53–69

Li W, Yan J (2021) Probability distribution of dependency distance based on a Treebank of. Japanese EFL Learners’ Interlanguage. J. Quant. Linguist. 28(2):172–186. https://doi.org/10.1080/09296174.2020.1754611

Article   MathSciNet   Google Scholar  

Linacre JM (2002) Optimizing rating scale category effectiveness. J. Appl. Meas. 3(1):85–106

PubMed   Google Scholar  

Linacre JM (1994) Constructing measurement with a Many-Facet Rasch Model. In Wilson M (ed) Objective measurement: Theory into practice, Volume 2 (pp. 129–144). Norwood, NJ: Ablex

Liu H (2008) Dependency distance as a metric of language comprehension difficulty. J. Cognitive Sci. 9:159–191

Liu H, Xu C, Liang J (2017) Dependency distance: A new perspective on syntactic patterns in natural languages. Phys. Life Rev. 21. https://doi.org/10.1016/j.plrev.2017.03.002

Loukina A, Madnani N, Cahill A, et al. (2020) Using PRMSE to evaluate automated scoring systems in the presence of label noise. Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, Seattle, WA, USA → Online, 10 July, pp. 18–29. Association for Computational Linguistics

Lu X (2010) Automatic analysis of syntactic complexity in second language writing. Int. J. Corpus Linguist. 15:474–496

Lu X (2012) The relationship of lexical richness to the quality of ESL learners’ oral narratives. Mod. Lang. J. 96:190–208

Lu X (2017) Automated measurement of syntactic complexity in corpus-based L2 writing research and implications for writing assessment. Lang. Test. 34:493–511

Lu X, Hu R (2022) Sense-aware lexical sophistication indices and their relationship to second language writing quality. Behav. Res. Method. 54:1444–1460. https://doi.org/10.3758/s13428-021-01675-6

Ministry of Health, Labor, and Welfare of Japan (2022) Retrieved from: https://www.mhlw.go.jp/stf/newpage_30367.html

Mizumoto A, Eguchi M (2023) Exploring the potential of using an AI language model for automated essay scoring. Res. Methods Appl. Linguist. 3:100050

Okgetheng B, Takeuchi K (2024) Estimating Japanese Essay Grading Scores with Large Language Models. Proceedings of 30th Annual Conference of the Language Processing Society in Japan, March 2024

Ortega L (2015) Second language learning explained? SLA across 10 contemporary theories. In VanPatten B, Williams J (ed) Theories in Second Language Acquisition: An Introduction

Rae JW, Borgeaud S, Cai T, et al. (2021) Scaling Language Models: Methods, Analysis & Insights from Training Gopher. ArXiv, abs/2112.11446

Read J (2000) Assessing vocabulary. Cambridge University Press. https://doi.org/10.1017/CBO9780511732942

Rudner LM, Liang T (2002) Automated Essay Scoring Using Bayes’ Theorem. J. Technol., Learning and Assessment, 1 (2)

Sakoda K, Hosoi Y (2020) Accuracy and complexity of Japanese Language usage by SLA learners in different learning environments based on the analysis of I-JAS, a learners’ corpus of Japanese as L2. Math. Linguist. 32(7):403–418. https://doi.org/10.24701/mathling.32.7_403

Suzuki N (1999) Summary of survey results regarding comprehensive essay questions. Final report of “Joint Research on Comprehensive Examinations for the Aim of Evaluating Applicability to Each Specialized Field of Universities” for 1996-2000 [shōronbun sōgō mondai ni kansuru chōsa kekka no gaiyō. Heisei 8 - Heisei 12-nendo daigaku no kaku senmon bun’ya e no tekisei no hyōka o mokuteki to suru sōgō shiken no arikata ni kansuru kyōdō kenkyū’ saishū hōkoku-sho]. University Entrance Examination Section Center Research and Development Department [Daigaku nyūshi sentā kenkyū kaihatsubu], 21–32

Taghipour K, Ng HT (2016) A neural approach to automated essay scoring. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, 1–5 November, pp. 1882–1891. Association for Computational Linguistics

Takeuchi K, Ohno M, Motojin K, Taguchi M, Inada Y, Iizuka M, Abo T, Ueda H (2021) Development of essay scoring methods based on reference texts with construction of research-available Japanese essay data. In IPSJ J 62(9):1586–1604

Ure J (1971) Lexical density: A computational technique and some findings. In Coultard M (ed) Talking about Text. English Language Research, University of Birmingham, Birmingham, England

Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. In Advances in Neural Information Processing Systems, Long Beach, CA, 4–7 December, pp. 5998–6008, Curran Associates, Inc., Red Hook, NY

Watanabe H, Taira Y, Inoue Y (1988) Analysis of essay evaluation data [Shōronbun hyōka dēta no kaiseki]. Bulletin of the Faculty of Education, University of Tokyo [Tōkyōdaigaku kyōiku gakubu kiyō], Vol. 28, 143–164

Yao S, Yu D, Zhao J, et al. (2023) Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36

Zenker F, Kyle K (2021) Investigating minimum text lengths for lexical diversity indices. Assess. Writ. 47:100505. https://doi.org/10.1016/j.asw.2020.100505

Zhang Y, Warstadt A, Li X, et al. (2021) When do you need billions of words of pretraining data? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online, pp. 1112-1125. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.90

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This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.

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Li, W., Liu, H. Applying large language models for automated essay scoring for non-native Japanese. Humanit Soc Sci Commun 11 , 723 (2024). https://doi.org/10.1057/s41599-024-03209-9

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