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Compare and contrast is a common form of academic writing, either as an essay type on its own, or as part of a larger essay which includes one or more paragraphs which compare or contrast. This page gives information on what a compare and contrast essay is , how to structure this type of essay, how to use compare and contrast structure words , and how to make sure you use appropriate criteria for comparison/contrast . There is also an example compare and contrast essay on the topic of communication technology, as well as some exercises to help you practice this area.
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To compare is to examine how things are similar, while to contrast is to see how they differ. A compare and contrast essay therefore looks at the similarities of two or more objects, and the differences. This essay type is common at university, where lecturers frequently test your understanding by asking you to compare and contrast two theories, two methods, two historical periods, two characters in a novel, etc. Sometimes the whole essay will compare and contrast, though sometimes the comparison or contrast may be only part of the essay. It is also possible, especially for short exam essays, that only the similarities or the differences, not both, will be discussed. See the examples below.
There are two main ways to structure a compare and contrast essay, namely using a block or a point-by-point structure. For the block structure, all of the information about one of the objects being compared/contrasted is given first, and all of the information about the other object is listed afterwards. This type of structure is similar to the block structure used for cause and effect and problem-solution essays. For the point-by-point structure, each similarity (or difference) for one object is followed immediately by the similarity (or difference) for the other. Both types of structure have their merits. The former is easier to write, while the latter is generally clearer as it ensures that the similarities/differences are more explicit.
The two types of structure, block and point-by-point , are shown in the diagram below.
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Compare and contrast structure words are transition signals which show the similarities or differences. Below are some common examples.
When making comparisons or contrasts, it is important to be clear what criteria you are using. Study the following example, which contrasts two people. Here the criteria are unclear.
Although this sentence has a contrast transition , the criteria for contrasting are not the same. The criteria used for Aaron are height (tall) and strength (strong). We would expect similar criteria to be used for Bruce (maybe he is short and weak), but instead we have new criteria, namely appearance (handsome) and intelligence (intelligent). This is a common mistake for students when writing this type of paragraph or essay. Compare the following, which has much clearer criteria (contrast structure words shown in bold).
Below is a compare and contrast essay. This essay uses the point-by-point structure . Click on the different areas (in the shaded boxes to the right) to highlight the different structural aspects in this essay, i.e. similarities, differences, and structure words. This will highlight not simply the paragraphs, but also the thesis statement and summary , as these repeat the comparisons and contrasts contained in the main body.
Title: There have been many advances in technology over the past fifty years. These have revolutionised the way we communicate with people who are far away. Compare and contrast methods of communication used today with those which were used in the past.
Before the advent of computers and modern technology, people communicating over long distances used traditional means such as letters and the telephone. Nowadays we have a vast array of communication tools which can complete this task, ranging from email to instant messaging and video calls. While the present and previous means of communication are similar in their general form , they differ in regard to their speed and the range of tools available . One similarity between current and previous methods of communication relates to the form of communication. In the past, both written forms such as letters were frequently used, in addition to oral forms such as telephone calls. Similarly , people nowadays use both of these forms. Just as in the past, written forms of communication are prevalent, for example via email and text messaging. In addition, oral forms are still used, including the telephone, mobile phone, and voice messages via instant messaging services. However , there are clearly many differences in the way we communicate over long distances, the most notable of which is speed. This is most evident in relation to written forms of communication. In the past, letters would take days to arrive at their destination. In contrast , an email arrives almost instantaneously and can be read seconds after it was sent. In the past, if it was necessary to send a short message, for example at work, a memo could be passed around the office, which would take some time to circulate. This is different from the current situation, in which a text message can be sent immediately. Another significant difference is the range of communication methods. Fifty years ago, the tools available for communicating over long distances were primarily the telephone and the letter. By comparison , there are a vast array of communication methods available today. These include not only the telephone, letter, email and text messages already mentioned, but also video conferences via software such as Skype or mobile phone apps such as WeChat, and social media such as Facebook and Twitter. In conclusion, methods of communication have greatly advanced over the past fifty years. While there are some similarities, such as the forms of communication , there are significant differences, chiefly in relation to the speed of communication and the range of communication tools available . There is no doubt that technology will continue to progress in future, and the advanced tools which we use today may one day also become outdated.
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Below is a checklist for compare and contrast essays. Use it to check your own writing, or get a peer (another student) to help you.
The essay is a essay | ||
An appropriate is used, either or | ||
Compare and contrast are used accurately | ||
The for comparison/contrast are clear | ||
The essay has clear | ||
Each paragraph has a clear | ||
The essay has strong support (facts, reasons, examples, etc.) | ||
The conclusion includes a of the main points |
There is a downloadable graphic organiser for brainstorming ideas for compare and contrast essays in the writing resources section.
Find out how to write cause & effect essays in the next section.
Go back to the previous section about persuasion essays .
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Author: Sheldon Smith ‖ Last modified: 08 January 2022.
Sheldon Smith is the founder and editor of EAPFoundation.com. He has been teaching English for Academic Purposes since 2004. Find out more about him in the about section and connect with him on Twitter , Facebook and LinkedIn .
Compare & contrast essays examine the similarities of two or more objects, and the differences.
Cause & effect essays consider the reasons (or causes) for something, then discuss the results (or effects).
Discussion essays require you to examine both sides of a situation and to conclude by saying which side you favour.
Problem-solution essays are a sub-type of SPSE essays (Situation, Problem, Solution, Evaluation).
Transition signals are useful in achieving good cohesion and coherence in your writing.
Reporting verbs are used to link your in-text citations to the information cited.
Last Updated: May 12, 2023 Approved
This article was co-authored by Megan Morgan, PhD . Megan Morgan is a Graduate Program Academic Advisor in the School of Public & International Affairs at the University of Georgia. She earned her PhD in English from the University of Georgia in 2015. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article has 29 testimonials from our readers, earning it our reader-approved status. This article has been viewed 3,103,809 times.
The purpose of a compare and contrast essay is to analyze the differences and/or the similarities of two distinct subjects. A good compare/contrast essay doesn’t only point out how the subjects are similar or different (or even both!). It uses those points to make a meaningful argument about the subjects. While it can be a little intimidating to approach this type of essay at first, with a little work and practice, you can write a great compare-and-contrast essay!
To write a compare and contrast essay, try organizing your essay so you're comparing and contrasting one aspect of your subjects in each paragraph. Or, if you don't want to jump back and forth between subjects, structure your essay so the first half is about one subject and the second half is about the other. You could also write your essay so the first few paragraphs introduce all of the comparisons and the last few paragraphs introduce all of the contrasts, which can help emphasize your subjects' differences and similarities. To learn how to choose subjects to compare and come up with a thesis statement, keep reading! Did this summary help you? Yes No
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Juliann Urban has taught high school English and has previously held the positions of English tutor for at-risk high school students and lead teacher at a private K-12 tutoring center. She holds a bachelor's degree in English with a concentration in secondary education from Governors State University, an associate in arts degree from Moraine Valley Community College, and a professional educator license with senior high and middle school language arts endorsements.
Compare and contrast essay, compare and contrast essay outline, how to write a compare and contrast essay, compare and contrast essay example, lesson summary, how do you write a compare and contrast essay point-by-point.
To use the point-by-point method, come up with one main idea for each body paragraph. Discuss both of your subjects within each body paragraph by comparing and contrasting them in relation to paragraph's main point.
A good introduction for a compare and contrast essay begins with an attention-getter, introduces the two subjects that will be compared and contrasted, gives a preview of the main points that will be discussed in the body paragraphs, and ends with a thesis statement.
A compare and contrast essay outline should contain introduction, body, and concluding paragraphs. The exact outline depends on the method of organization that is used.
The point-by-point method uses a standard five-paragraph essay structure:
The block method uses a four-paragraph structure:
A compare and contrast essay is an essay that discusses or explains the similarities and differences between two subjects. Sometimes the guidelines for a compare and contrast essay will state which two subjects should be compared and contrasted, such as two stories which were read in class. Other times, it is left up to the student to decide which subjects to compare and contrast. In this case, it is best if the two subjects are from the same category like two books, two films, two animals, two sports, or two songs. It would be much more difficult to compare two subjects that belong to totally different categories, like Shakespeare and a zebra.
When choosing a topic, pick a subject that is interesting to you. It is easiest to write about topics that you are interested in, and it is helpful to already know a little bit about the topic in advance so that you can spend your time writing rather than researching. You should also give some thought to whether the topic will be interesting to your reader, likely your teacher. An interesting topic is not usually a requirement of a compare and contrast essay, but an essay that is interesting or entertaining to read will likely reflect that in the essay's final grade.
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To compare is to explain the similarities between two subjects. To contrast is to explain the differences between two subjects. These similarities and differences will become the main ideas of your body paragraphs. When choosing which similarities and differences to discuss in your essay, be sure they are significant or thought-provoking. The goal of your essay should be to leave the reader with a new outlook on your subjects, not to tell them information they already know.
There are two methods of organization that may be used to arrange your ideas within the body paragraphs, the point-by-point method and the block method:
Despite which method or organization you use, the order of your main points is the same: your strongest point comes last, your second strongest point comes first, and your weakest point goes in the middle. So, if you are using the point-by-point method, arrange your paragraphs with your strongest point in the third body paragraph, your second strongest point in the first body paragraph, and your weakest point in the second body paragraph. If you are using the block method, place your strongest point as the last main idea in the body paragraph, the second strongest point as the first main idea, and the weakest point in the middle of the body paragraph. The reasoning behind this is simple. You are sandwiching your weakest point between the two strongest points so that your essay is compelling at the beginning and at the end.
Point-by-point method:
Introduction
Body paragraph 1
Body paragraph 2
Body paragraph 3
Block method:
As mentioned above, the structure and content of the body paragraphs will depend on which method, point-by-point or block, is used. However, body paragraphs should contain these elements regardless of which method is used.
Below is a body paragraph from a sample compare and contrast essay from Lumen Learning at lumenlearning.com. Notice how it uses the point-by-point method to compare and contrast the cultural diversity and cost of living in Washington, D.C., and London.
"Both cities are rich in world and national history, though they developed on very different time lines. London, for example, has a history that dates back over two thousand years. It was part of the Roman Empire and known by the similar name, Londinium. It was not only one of the northernmost points of the Roman Empire but also the epicenter of the British Empire where it held significant global influence from the early sixteenth century on through the early twentieth century. Washington, DC, on the other hand, has only formally existed since the late eighteenth century. Though Native Americans inhabited the land several thousand years earlier, and settlers inhabited the land as early as the sixteenth century, the city did not become the capital of the United States until the 1790s. From that point onward to today, however, Washington, DC, has increasingly maintained significant global influence. Even though both cities have different histories, they have both held, and continue to hold, significant social influence in the economic and cultural global spheres."
A compare and contrast essay discusses that similarities and differences between two subjects. The two main methods for organizing your ideas within the essay are the point-by-point method (five paragraphs) and the block method (four paragraphs). The method you choose will determine the outline of your essay, but all essays should contain an introduction that begins with an attention-getter, previews the main points, and ends with a thesis statement; body paragraphs that begin with topic sentences, explain the main ideas, and end with concluding statements; and a conclusion that restates the thesis, summarizes the main points, and leaves the reader with a lingering thought.
What is a compare and contrast essay.
Have you ever been accused of comparing apples to oranges and wondered what that meant? Rachel has, and now her English teacher is asking for a compare and contrast essay.
Understanding what a compare and contrast essay is makes it much easier to write one! A compare and contrast essay is an essay in which at least two subjects (characters, themes, movies) are discussed in terms of their similarities and differences in order to describe a relationship among them.
Rachel could write a compare and contrast essay describing the similarities and differences between two rival sports teams, or two fictional characters, or two books. She could, theoretically, write a compare and contrast essay about a pencil and Thor, but compare and contrast essays work out best when the two subjects belong to the same broader category.
Let's briefly review general essay structure, then discuss what is specific to a compare and contrast essay.
An essay is a way of organizing writing to support or prove a point, called the thesis. The most common essay structure discussed in schools is the five-paragraph essay. In this structure, the essay begins with:
This general essay structure can be used for a number of different purposes: to persuade, to describe, or to compare and contrast.
Now, let's discuss what is specific to a compare and contrast essay. Most people simply use the word ''compare'' when they mean both compare and contrast, but the two words actually have specific, separate, and opposite meanings.
A good compare and contrast essay engages the reader by showing how these points enrich the way we think about the two subjects. Focus on similarities and differences that are relevant and significant.
For example, say Rachel is writing a compare and contrast essay on the two fictional characters Hamlet and Homer Simpson. Her points should go beyond the obvious or superficial. She wouldn't write an essay arguing that these two characters are similar because they are both human males, yet different because they live on different continents.
However, she might argue that they are similar because they are both motivated by their appetites and lack long-term planning skills, but are differentiated by their relationships to their families.
Keep the essay's length in mind when choosing a topic. It's better to have too much information and need to be selective, than having too little to say. Look for subjects that could have interesting, unusual, or unexpected similarities and differences.
Use a brainstorming technique such as mind mapping or a Venn diagram to help you write down and organize your ideas at this stage. Write down any points of comparison or contrast as they occur. Then, select body paragraph topics from among these points and conduct research on these.
After choosing the topic, consider the body paragraph organization. There are two general methods for organizing your compare and contrast body paragraphs.
The block method involves having two large body paragraphs. One will be the comparison paragraph that describes all of the points of comparison between the two essay subjects. The other will be the contrast paragraph that describes all points of contrast.
The general rule for ordering paragraphs in any essay is to end on the strongest paragraph, so order the two body paragraphs accordingly.
Each of these two paragraphs will likely have 2-3 points of comparison or contrast. Organize them with the strongest point coming last, the second strongest first, and the others organized logically in between.
The point-by-point method has the standard three (or more) body paragraphs, each discussing both subjects in terms of a single point, either a comparison or a contrast. In each paragraph, discuss both subjects (Hamlet and Homer in the example), but only a single point, either a comparison or a contrast.
For ordering your paragraphs in this method, the same rule applies: you should use your strongest paragraph last and your second strongest first.
Which method you use will depend on the amount of points that you want to make, but also the kinds of points you are making. For example, if you have an uneven number of points for each side (for example, lots of comparisons but few contrasts), then use the point-by-point method, since the block method would have one really long paragraph and one really short one, in this example. Just make sure you have something meaningful to say on both the compare side and the contrast side.
The conclusion of your essay will be a restatement of the points within the body paragraphs, as well as a description of how those points support the overall thesis.
We have left the description of the introduction for last because that is when you should write it: last. This section prepares the reader for the essay by introducing its contents, but you yourself won't know what you are introducing until after the essay is written.
Describe the points in the introduction and conclusion in the same order as they appear in the essay. If, in the Hamlet and Homer essay, Rachel's points come in the order of desires, planning, and family life, then they should be described in that order for her introduction and conclusion as well.
A compare and contrast essay describes a relationship between two subjects in terms of points of similarities ( comparisons ) and differences ( contrasts ).
The essay can be structured according to:
Order body paragraphs with the strongest one last, and the second strongest first. The points discussed should contribute to a deeper understanding of both subjects.
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Key Takeaways Essay and composition are both forms of academic writing that require critical thinking, analysis, and effective communication; essay is a more specific term that refers to a piece of writing that presents a thesis statement and supports it with evidence and analysis. The composition can encompass various types of writing, including essays, narratives, and descriptive pieces; an essay is a specific type of composition with a more structured format. An essay includes an introduction, body paragraphs, and a conclusion, while composition may not have a specific structure or format.
Similar reads, comparison table.
The essay’s main purpose is to cause the reader to reflect on a particular topic declaring the author’s opinion. | The composition’s main purpose is to describe the topic and express the author’s feelings. | |
An author’s position and thoughts on the current topic must be clearly understood from the essay. | The author can follow another author’s thoughts without adding his opinion on the composition’s subject. | |
The essay structure is not strong and can vary depending on the topic | The composition must follow a specific outline: introduction, body, and conclusion | |
Usually 2-3 pages (about 1500 words) | Usually larger than an essay – about 3-5 pages (1500-3000 words) | |
States the author’s position on a current topic clearly, and reveals the author’s mindset, visions, impressions, opinion | Analyzes the existing sources on the topic, expresses and compares other authors’ thoughts, expresses the author’s feelings about another author’s opinion |
What is composition, main differences between an essay and composition.
Last Updated : 11 June, 2023
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When it comes to writing, it’s important to use the correct terminology to effectively communicate your message. Two terms that are often used interchangeably are “essay” and “composition.” But which one is the right word to use? The answer is both, as they refer to similar but slightly different forms of writing.
An essay is a piece of writing that presents an argument or point of view on a specific topic. It typically consists of an introduction, body paragraphs, and a conclusion. Essays can be formal or informal, and can range in length from a few paragraphs to several pages. For those who might be struggling with essay writing or just need a hand, there are essay writers for hire who specialize in crafting these types of writing.
A composition, on the other hand, is a broader term that refers to any piece of writing. It can include essays, but also encompasses other forms such as poetry, short stories, and even music. Compositions can be written for a variety of purposes, including entertainment, education, and self-expression.
Throughout this article, we’ll explore the similarities and differences between essays and compositions, as well as provide tips for writing each effectively.
An essay is a piece of writing that presents an argument or a point of view on a particular topic. It is typically a short piece of writing that is written in a formal style and is structured in a way that allows the reader to follow the author’s argument or point of view. Essays can be written on a wide range of topics, from politics and social issues to literature and science.
Essays are often used as a way for students to demonstrate their understanding of a particular subject or to showcase their writing skills. They are also commonly used in academic settings as a way for scholars to present their research or to engage in intellectual discourse with their peers.
A composition is a piece of writing that is focused on a particular topic or subject. Like an essay, it is typically written in a formal style and is structured in a way that allows the reader to follow the author’s argument or point of view. However, compositions are often longer than essays and may be more detailed and comprehensive in their coverage of a particular subject.
Compositions can take many different forms, including research papers, reports, and literary analyses. They are often used in academic and professional settings as a way for individuals to communicate their ideas and findings to others in their field.
Unlike essays, which are often focused on presenting an argument or point of view, compositions may be more focused on presenting information or exploring a particular topic in depth.
When it comes to writing, using the correct terminology is crucial. The words “essay” and “composition” are often used interchangeably, but they actually have distinct meanings. In this section, we’ll explore how to properly use these words in a sentence.
An essay is a piece of writing that presents an argument or discusses a particular topic. When using the word “essay” in a sentence, it’s important to make sure it’s being used in the correct context. Here are a few examples:
As you can see, the word “essay” is typically used to refer to a specific type of writing. It’s important to use it in a way that accurately reflects its meaning.
The word “composition” can refer to a few different things when it comes to writing. It can be used to describe a piece of music or art, but in the context of writing, it typically refers to a written work. Here are a few examples of how to use “composition” in a sentence:
Just like with “essay,” it’s important to use “composition” in a way that accurately reflects its meaning. In the context of writing, it typically refers to a written work that is structured and thoughtfully put together.
In order to further understand the differences between an essay and a composition, it can be helpful to see how these terms are used in different contexts. Here are some examples of how these words can be used in sentences:
When it comes to writing, the terms essay and composition are often used interchangeably. However, this is a common mistake that can lead to confusion and miscommunication. Here are some of the most common mistakes people make when using essay and composition interchangeably, along with explanations of why they are incorrect:
One of the most common mistakes people make is using the term “essay” to refer to any type of writing. While essays are a type of composition, not all compositions are essays. For example, a research paper or a thesis is not an essay, but rather a different type of composition that requires a different structure and approach.
Another common mistake is using the term “composition” to refer only to formal writing, such as academic papers or business reports. However, compositions can take many different forms, from creative writing to personal narratives. Using “composition” only to refer to formal writing can limit your understanding of the different types of writing that exist.
Essays and compositions serve different purposes, and it’s important to understand the difference. Essays are typically used to persuade or inform the reader about a specific topic, while compositions can serve a variety of purposes, such as expressing emotions, telling a story, or describing an experience. Confusing the purpose of essays and compositions can lead to writing that is ineffective or off-topic.
Finally, one of the most common mistakes people make is failing to consider the audience and context when using essay and composition interchangeably. Different types of writing require different approaches and styles, depending on the audience and context. For example, a personal narrative written for a creative writing class will require a different approach than a business report written for a professional audience. It’s important to consider these factors when choosing the appropriate type of writing.
To avoid making these mistakes in the future, consider the purpose of your writing, the audience and context, and the appropriate type of writing for your needs. By doing so, you can ensure that your writing is effective, clear, and communicates your message to your intended audience.
Choosing between an essay and a composition can depend on the context in which they are used. Both terms are often used interchangeably, but the context can make a difference in the choice.
Here are some examples of different contexts and how the choice between essay and composition might change:
In each of these contexts, the choice between essay and composition can depend on the specific requirements of the assignment or the preferences of the writer. It’s important to understand the nuances of each term and how they are used in different contexts in order to choose the most appropriate one for your writing.
While essay and composition are often used interchangeably, there are some exceptions to the rules that should be noted. Here are some situations where the rules for using essay and composition might not apply:
In academic writing, the term “essay” is typically used to refer to a shorter piece of writing that is assigned as homework or as part of an exam. However, in some cases, a longer piece of academic writing may be referred to as a composition. For example, a thesis or dissertation may be referred to as a composition.
In creative writing, the term “composition” is often used to refer to a longer piece of writing that is more structured and formal than an essay. For example, a novel or a screenplay may be referred to as a composition. In this context, “essay” is typically used to refer to a shorter piece of writing, such as a personal essay or a memoir.
There may be regional differences in the way that the terms “essay” and “composition” are used. For example, in some parts of the world, “essay” may be the preferred term for all types of writing, while in other parts of the world, “composition” may be used more frequently.
Ultimately, the choice to use “essay” or “composition” may come down to personal preference. Some writers may prefer the more formal connotations of “composition,” while others may prefer the more casual connotations of “essay.”
Regardless of the context in which they are used, both essay and composition refer to a piece of writing that expresses the author’s thoughts and opinions on a particular topic. By understanding the exceptions to the rules, writers can choose the term that best fits their intended meaning and audience.
Now that we have a better understanding of the difference between an essay and a composition, it’s time to put that knowledge to the test. Here are some practice exercises to help you improve your understanding and use of these terms in sentences:
Fill in the blank with either “essay” or “composition” to complete the sentence correctly:
Answer Key:
Read the following sentences and identify if the underlined word is an “essay” or a “composition”:
By practicing these exercises, you can improve your understanding and usage of “essay” and “composition.” Keep in mind that while they may be used interchangeably in some contexts, there are distinct differences between the two terms that should be understood in order to communicate effectively.
After examining the differences between essays and compositions, it is clear that the two terms are often used interchangeably but have distinct differences. Essays are typically more formal and structured, while compositions can be more creative and free-flowing. Additionally, essays often require research and citations, while compositions do not necessarily require them.
It is important for writers to understand the nuances between these two terms in order to effectively communicate their ideas and meet the expectations of their audience. By utilizing proper grammar and language use, writers can enhance the clarity and impact of their writing.
As with any skill, the ability to write effectively requires ongoing learning and practice. By continuing to study grammar and language use, writers can improve their writing and better connect with their readers.
Shawn Manaher is the founder and CEO of The Content Authority. He’s one part content manager, one part writing ninja organizer, and two parts leader of top content creators. You don’t even want to know what he calls pancakes.
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Methodology
Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.
What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .
There are five key steps to writing a literature review:
A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.
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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.
When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:
Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.
Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.
You can also check out our templates with literature review examples and sample outlines at the links below.
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Before you begin searching for literature, you need a clearly defined topic .
If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .
Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.
Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:
You can also use boolean operators to help narrow down your search.
Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.
You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.
For each publication, ask yourself:
Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.
You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.
As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.
It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.
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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.
There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.
Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.
For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.
If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:
A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.
You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.
The introduction should clearly establish the focus and purpose of the literature review.
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.
As you write, you can follow these tips:
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.
When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !
This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.
Scribbr slides are free to use, customize, and distribute for educational purposes.
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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
Statistics
Research bias
A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .
It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.
There are several reasons to conduct a literature review at the beginning of a research project:
Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.
The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .
A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .
An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved June 7, 2024, from https://www.scribbr.com/dissertation/literature-review/
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Ideas to inspire every young writer!
High school students generally do a lot of writing, learning to use language clearly, concisely, and persuasively. When it’s time to choose an essay topic, though, it’s easy to come up blank. If that’s the case, check out this huge round-up of essay topics for high school. You’ll find choices for every subject and writing style.
Argumentative essay topics for high school.
When writing an argumentative essay, remember to do the research and lay out the facts clearly. Your goal is not necessarily to persuade someone to agree with you, but to encourage your reader to accept your point of view as valid. Here are some possible argumentative topics to try. ( Here are 100 more compelling argumentative essay topics. )
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A cause-and-effect essay is a type of argumentative essay. Your goal is to show how one specific thing directly influences another specific thing. You’ll likely need to do some research to make your point. Here are some ideas for cause-and-effect essays. ( Get a big list of 100 cause-and-effect essay topics here. )
As the name indicates, in compare-and-contrast essays, writers show the similarities and differences between two things. They combine descriptive writing with analysis, making connections and showing dissimilarities. The following ideas work well for compare-contrast essays. ( Find 80+ compare-contrast essay topics for all ages here. )
Bring on the adjectives! Descriptive writing is all about creating a rich picture for the reader. Take readers on a journey to far-off places, help them understand an experience, or introduce them to a new person. Remember: Show, don’t tell. These topics make excellent descriptive essays.
Expository essays set out clear explanations of a particular topic. You might be defining a word or phrase or explaining how something works. Expository or informative essays are based on facts, and while you might explore different points of view, you won’t necessarily say which one is “better” or “right.” Remember: Expository essays educate the reader. Here are some expository and informative essay topics to explore. ( See 70+ expository and informative essay topics here. )
Humorous essays can take on any form, like narrative, persuasive, or expository. You might employ sarcasm or satire, or simply tell a story about a funny person or event. Even though these essay topics are lighthearted, they still take some skill to tackle well. Give these ideas a try.
Literary essays analyze a piece of writing, like a book or a play. In high school, students usually write literary essays about the works they study in class. These literary essay topic ideas focus on books students often read in high school, but many of them can be tweaked to fit other works as well.
Think of a narrative essay like telling a story. Use some of the same techniques that you would for a descriptive essay, but be sure you have a beginning, middle, and end. A narrative essay doesn’t necessarily need to be personal, but they often are. Take inspiration from these narrative and personal essay topics.
Persuasive essays are similar to argumentative , but they rely less on facts and more on emotion to sway the reader. It’s important to know your audience, so you can anticipate any counterarguments they might make and try to overcome them. Try these topics to persuade someone to come around to your point of view. ( Discover 60 more intriguing persuasive essay topics here. )
A research essay is a classic high school assignment. These papers require deep research into primary source documents, with lots of supporting facts and evidence that’s properly cited. Research essays can be in any of the styles shown above. Here are some possible topics, across a variety of subjects.
Plus, check out the ultimate guide to student writing contests .
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Practice making well-reasoned arguments using research and facts. Continue Reading
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The compare-and-contrast essay starts with a thesis that clearly states the two subjects that are to be compared, contrasted, or both and the reason for doing so. The thesis could lean more toward comparing, contrasting, or both. Remember, the point of comparing and contrasting is to provide useful knowledge to the reader. Take the following thesis as an example that leans more toward contrasting.
Thesis statement : Organic vegetables may cost more than those that are conventionally grown, but when put to the test, they are definitely worth every extra penny.
Here the thesis sets up the two subjects to be compared and contrasted (organic versus conventional vegetables), and it makes a claim about the results that might prove useful to the reader.
You may organize compare-and-contrast essays in one of the following two ways:
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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.
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 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.
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.
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.
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.
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).
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.
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.
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).
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.
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 ).
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.
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.
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.
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.
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Model 1: GPT-4 | ||
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Model 2: GPT-4 + 17 measures | ||
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Model 3: GPT-4 + MATTR | Model 4: GPT-4 + LD | Model 5: GPT-4 + LS |
Model 6: GPT-4 + MLC | Model 7: GPT-4 + VPT | Model 8: GPT-4 + CT |
Model 9: GPT-4 + DCT | Model 10: GPT-4 + CNT | Model 11: GPT-4 + ACC |
Model 12: GPT-4 + CPC | Model 13: GPT-4 + MDD | Model 14: GPT-4 + SOPT |
Model 15: GPT-4 + SOPK | Model 16: GPT-4 + word2vec | |
Model 17: GPT-4 + IMM | Model 18: GPT-4 + GER |
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.
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.
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This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.
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Five high school students helped our tech columnist test a ChatGPT detector coming from Turnitin to 2.1 million teachers. It missed enough to get someone in trouble.
High school senior Lucy Goetz got the highest possible grade on an original essay she wrote about socialism. So imagine her surprise when I told her that a new kind of educational software I’ve been testing claimed she got help from artificial intelligence.
A new AI-writing detector from Turnitin — whose software is already used by 2.1 million teachers to spot plagiarism — flagged the end of her essay as likely being generated by ChatGPT .
“Say what?” says Goetz, who swears she didn’t use the AI writing tool to cheat. “I’m glad I have good relationships with my teachers.”
After months of sounding the alarm about students using AI apps that can churn out essays and assignments, teachers are getting AI technology of their own. On April 4, Turnitin is activating the software I tested for some 10,700 secondary and higher-educational institutions, assigning “generated by AI” scores and sentence-by-sentence analysis to student work. It joins a handful of other free detectors already online. For many teachers I’ve been hearing from, AI detection offers a weapon to deter a 21st-century form of cheating.
But AI alone won’t solve the problem AI created. The flag on a portion of Goetz’s essay was an outlier, but shows detectors can sometimes get it wrong — with potentially disastrous consequences for students. Detectors are being introduced before they’ve been widely vetted, yet AI tech is moving so fast, any tool is likely already out of date.
It’s a pivotal moment for educators: Ignore AI and cheating could go rampant. Yet even Turnitin’s executives tell me that treating AI purely as the enemy of education makes about as much sense in the long run as trying to ban calculators.
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Ahead of Turnitin’s launch this week, the company says 2 percent of customers have asked it not to display the AI writing score on student work. That includes a "significant majority” of universities in the United Kingdom, according to UCISA , a professional body for digital educators.
To see what’s at stake, I asked Turnitin for early access to its software. Five high school students, including Goetz, volunteered to help me test it by creating 16 samples of real, AI-fabricated and mixed-source essays to run past Turnitin’s detector.
The result? It got over half of them at least partly wrong. Turnitin accurately identified six of the 16 — but failed on three, including a flag on 8 percent of Goetz’s original essay. And I’d give it only partial credit on the remaining seven, where it was directionally correct but misidentified some portion of ChatGPT-generated or mixed-source writing.
Turnitin claims its detector is 98 percent accurate overall. And it says situations such as what happened with Goetz’s essay, known as a false positive, happen less than 1 percent of the time, according to its own tests.
Turnitin also says its scores should be treated as an indication, not an accusation . Still, will millions of teachers understand they should treat AI scores as anything other than fact? After my conversations with the company, it added a caution flag to its score that reads, “Percentage may not indicate cheating. Review required.”
“Our job is to create directionally correct information for the teacher to prompt a conversation,” Turnitin chief product officer Annie Chechitelli tells me. “I’m confident enough to put it out in the market, as long as we’re continuing to educate educators on how to use the data.” She says the company will keep adjusting its software based on feedback and new AI advancements.
The question is whether that will be enough. “The fact that the Turnitin system for flagging AI text doesn’t work all the time is concerning,” says Rebecca Dell, who teaches Goetz’s AP English class in Concord, Calif. “I’m not sure how schools will be able to definitively use the checker as ‘evidence’ of students using unoriginal work.”
Unlike accusations of plagiarism, AI cheating has no source document to reference as proof. “This leaves the door open for teacher bias to creep in,” says Dell.
For students, that makes the prospect of being accused of AI cheating especially scary. “There is no way to prove that you didn’t cheat unless your teacher knows your writing style, or trusts you as a student,” says Goetz.
Spotting AI writing sounds deceptively simple. When a colleague recently asked me if I could detect the difference between real and ChatGPT-generated emails, I didn’t perform very well.
Detecting AI writing with software involves statistics. And statistically speaking, the thing that makes AI distinct from humans is that it’s “extremely consistently average,” says Eric Wang, Turnitin’s vice president of AI.
Systems such as ChatGPT work like a sophisticated version of auto-complete, looking for the most probable word to write next. “That’s actually the reason why it reads so naturally: AI writing is the most probable subset of human writing,” he says.
Turnitin’s detector “identifies when writing is too consistently average,” Wang says.
The challenge is that sometimes a human writer may actually look consistently average.
On economics, math and lab reports, students tend to hew to set styles, meaning they’re more likely to be misidentified as AI writing, says Wang. That’s likely why Turnitin erroneously flagged Goetz’s essay, which veered into economics. (“My teachers have always been fairly impressed with my writing,” says Goetz.)
Wang says Turnitin worked to tune its systems to err on the side of requiring higher confidence before flagging a sentence as AI. I saw that develop in real time: I first tested Goetz’s essay in late January, and the software identified much more of it — about 50 percent — as being AI generated. Turnitin ran my samples through its system again in late March, and that time only flagged 8 percent of Goetz’s essay as AI-generated.
But tightening up the software’s tolerance came with a cost: Across the second test of my samples, Turnitin missed more actual AI writing. “We’re really emphasizing student safety,” says Chechitelli.
Say hello to your new tutor: It’s ChatGPT
Turnitin does perform better than other public AI detectors I tested. One introduced in February by OpenAI, the company that invented ChatGPT, got eight of our 16 test samples wrong. (Independent tests of other detectors have declared they “ fail spectacularly .”)
Turnitin’s detector faces other important technical limitations, too. In the six samples it got completely right, they were all clearly 100 percent student work or produced by ChatGPT. But when I tested it with essays from mixed AI and human sources, it often misidentified the individual sentences or missed the human part entirely. And it couldn’t spot the ChatGPT in papers we ran through Quillbot, a paraphrasing program that remixes sentences.
What’s more, Turnitin’s detector may already be behind the state of the AI art. My student helpers created samples with ChatGPT, but since they did the writing, the app has gotten a software update called GPT-4 with more creative and stylistic capabilities. Google also introduced a new AI bot called Bard . Wang says addressing them is on his road map.
Some AI experts say any detection efforts are at best setting up an arms race between cheaters and detectors. “I don’t think a detector is long-term reliable,” says Jim Fan, an AI scientist at Nvidia who used to work at OpenAI and Google.
“The AI will get better, and will write in ways more and more like humans. It is pretty safe to say that all of these little quirks of language models will be reduced over time,” he says.
Given the potential — even at 1 percent — of being wrong, why release an AI detector into software that will touch so many students?
“Teachers want deterrence,” says Chechitelli. They’re extremely worried about AI and helping them see the scale of the actual problem will “bring down the temperature.”
Some educators worry it will actually raise the temperature.
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Make sure they have enough similarities and differences to make a meaningful comparison. 2. Brainstorm key points: Once you have chosen the subjects, brainstorm the key points you want to compare and contrast. These could include characteristics, features, themes, or arguments related to each subject. 3.
Making effective comparisons. As the name suggests, comparing and contrasting is about identifying both similarities and differences. You might focus on contrasting quite different subjects or comparing subjects with a lot in common—but there must be some grounds for comparison in the first place. For example, you might contrast French ...
Making a Venn diagram or a chart can help you quickly and efficiently compare and contrast two or more things or ideas. To make a Venn diagram, simply draw some overlapping circles, one circle for each item you're considering. In the central area where they overlap, list the traits the two items have in common.
The Purpose of Comparison and Contrast in Writing. Comparison in writing discusses elements that are similar, while contrast in writing discusses elements that are different. A compare-and-contrast essay, then, analyzes two subjects by comparing them, contrasting them, or both.. The key to a good compare-and-contrast essay is to choose two or more subjects that connect in a meaningful way.
The purpose of writing a comparison or contrast essay is not to state the obvious but rather to illuminate subtle differences or unexpected similarities between two subjects. The thesis should clearly state the subjects that are to be compared, contrasted, or both, and it should state what is to be learned from doing so. ...
An academic compare and contrast essay looks at two or more subjects, ideas, people, or objects, compares their likeness, and contrasts their differences. It's an informative essay that provides insights on what is similar and different between the two items. Depending on the essay's instructions, you can focus solely on comparing or ...
The Structure of a Compare/Contrast Essay. The compare-and-contrast essay starts with a thesis that clearly states the two subjects that are to be compared, contrasted, or both and the reason for doing so. The thesis could lean more toward comparing, contrasting, or both. Remember, the point of comparing and contrasting is to provide useful ...
Writing a Comparison-and-Contrast Essay. First, choose whether you want to compare seemingly disparate subjects, contrast seemingly similar subjects, or compare and contrast subjects. Once you have decided on a topic, introduce it with an engaging opening paragraph. Your thesis should come at the end of the introduction, and it should establish ...
A compare-and-contrast essay analyzes two subjects by either comparing them, contrasting them, or both. The purpose of writing a comparison or contrast essay is not to state the obvious but rather to illuminate subtle differences or unexpected similarities between two subjects. The thesis should clearly state the subjects that are to be ...
Compare and contrast essays examine topics from multiple viewpoints. This kind of essay, often assigned in middle school and high school, teaches students about the analytical writing process and prepares them for more advanced forms of academic writing. Compare and contrast essays are relatively easy to write if you follow a simple step-by-step approach.
Moreover, a comparative analysis essay discusses the similarities and differences of themes, items, events, views, places, concepts, etc. For example, you can compare two different novels (e.g., The Adventures of Huckleberry Finn and The Red Badge of Courage). However, a comparative essay is not limited to specific topics.
To compare is to examine how things are similar, while to contrast is to see how they differ. A compare and contrast essay therefore looks at the similarities of two or more objects, and the differences. This essay type is common at university, where lecturers frequently test your understanding by asking you to compare and contrast two theories ...
4. Outline your body paragraphs based on point-by-point comparison. This is the more common method used in the comparison and contrast essay. [6] You can write a paragraph about each characteristic of both locations, comparing the locations in the same paragraph.
Compare and Contrast Essay Outline. The point-by-point method uses a standard five-paragraph essay structure: Introduction (contains the attention-getter, preview of main points, and thesis) Body ...
Comparison Table. The essay's main purpose is to cause the reader to reflect on a particular topic declaring the author's opinion. The composition's main purpose is to describe the topic and express the author's feelings. An author's position and thoughts on the current topic must be clearly understood from the essay.
A compare and contrast essay does two things: It discusses the similarities and differences of at least two different things. First, you must find a basis of comparison to be sure that the two things have enough in common. After that, you identify their differences. You may structure the compare and contrast essay using either the alternating ...
The Comparison and Contrast Guide outlines the characteristics of the genre and provides direct instruction on the methods of organizing, gathering ideas, and writing comparison and contrast essays.
When it comes to writing, the terms essay and composition are often used interchangeably. However, this is a common mistake that can lead to confusion and miscommunication. Here are some of the most common mistakes people make when using essay and composition interchangeably, along with explanations of why they are incorrect: 1.
1. Remember a time when you had a misunderstanding with someone because of miscommunication. This could be something that happened between you and a friend, a roommate, a family member, or someone at school. Write about the situation and the dif erent ways you and the other person understood the situation. 2.
An essay is a focused piece of writing designed to inform or persuade. There are many different types of essay, but they are often defined in four categories: argumentative, expository, narrative, and descriptive essays. Argumentative and expository essays are focused on conveying information and making clear points, while narrative and ...
Next, the body includes paragraphs that explore the similarities and differences. Finally, a concluding paragraph restates the thesis, draws any necessary inferences, and asks any remaining questions. A compare and contrast essay example can be an opinion piece comparing two things and making a conclusion about which is better. For example ...
A compare and contrast essay requires deep thought. The considerations you make can deliver great insight about your subject of choice. Here are some tips to help. ... unwieldy paragraphs). However, if you're focusing on a single comparison point or writing a timed essay, this can be an easy, effective, and succinct method to organize your ...
Crucially, citation practices do not differ between the two styles of paper. However, for your convenience, we have provided two versions of our APA 7 sample paper below: one in student style and one in professional style. Note: For accessibility purposes, we have used "Track Changes" to make comments along the margins of these samples.
Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
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The following ideas work well for compare-contrast essays. ( Find 80+ compare-contrast essay topics for all ages here.) Public and private schools. Capitalism vs. communism. Monarchy or democracy. Dogs vs. cats as pets. WeAreTeachers. Paper books or e-books. Two political candidates in a current race.
By Lisa Lieberman. June 7, 2024. It was getting toward the end of this recent semester, and I was at a loss. Either one of two things was happening: My freshman composition students' writing had ...
The compare-and-contrast essay starts with a thesis that clearly states the two subjects that are to be compared, contrasted, or both and the reason for doing so. The thesis could lean more toward comparing, contrasting, or both. Remember, the point of comparing and contrasting is to provide useful knowledge to the reader.
A dataset comprising a total of 1400 essays from the story writing tasks was collected. ... We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency ...
It flagged an innocent student. Five high school students helped our tech columnist test a ChatGPT detector coming from Turnitin to 2.1 million teachers. It missed enough to get someone in trouble ...