coherent essay

Achieving coherence

“A piece of writing is coherent when it elicits the response: ‘I follow you. I see what you mean.’ It is incoherent when it elicits the response: ‘I see what you're saying here, but what has it got to do with the topic at hand or with what you just told me above?’ ” - Johns, A.M

Transitions

Parallelism, challenge task, what is coherence.

Coherence in a piece of writing means that the reader can easily understand it. Coherence is about making everything flow smoothly. The reader can see that everything is logically arranged and connected, and relevance to the central focus of the essay is maintained throughout.

Repetition in a piece of writing does not always demonstrate cohesion.   Study these sentences:

So, how does repetition as a cohesive device work?

When a pronoun is used, sometimes what the pronoun refers to (ie, the referent) is not always clear. Clarity is achieved by  repeating a key noun or synonym . Repetition is a cohesive device used deliberately to improve coherence in a text.

In the following text, decide ifthe referent for the pronoun  it   is clear. Otherwise, replace it  with the key noun English  where clarity is needed.

Click here to view the revised text.

Suggested improvement

English has almost become an international language. Except for Chinese, more people speak it (clear reference; retain)  than any other language. Spanish is the official language of more countries in the world, but more countries have English ( it is replaced with a key noun) as their official or unofficial second language. More than 70% of the world's mail is written in English ( it is replaced with a key noun).  It (clear reference; retain) is the primary language on the Internet.

Sometimes, repetition of a key noun is preferred even when the reference is clear. In the following text, it is clear that it  refers to the key noun gold , but when used throughout the text, the style becomes monotonous.

Improved text: Note where the key noun gold is repeated. The deliberate repetition creates interest and adds maturity to the writing style.

Gold , a precious metal, is prized for two important characteristics. First of all, gold has a lustrous beauty that is resistant to corrosion. Therefore, it is suitable for jewellery, coins and ornamental purposes. Gold never needs to be polished and will remain beautiful forever. For example, a Macedonian coin remains as untarnished today as the day it was made 23 centuries ago. Another important characteristic of gold is its usefulness to industry and science. For many years, it has been used in hundreds of industrial applications. The most recent use of gold is in astronauts’ suits. Astronauts wear gold -plated shields when they go outside spaceships in space. In conclusion, gold is treasured not only for its beauty but also its utility.

Pronoun + Repetition of key noun

Sometimes, greater cohesion can be achieved by using a pronoun followed by an appropriate key noun or synonym (a word with a similar meaning).

Transitions are like traffic signals. They guide the reader from one idea to the next. They signal a range of relationships between sentences, such as comparison, contrast, example and result. Click here for a more comprehensive list of Transitions (Logical Organisers) .

Test yourself: How well do you understand transitions?

Which of the three alternatives should follow the transition or logical organiser in capital letters to complete the second sentence?

Using transitions/logical organisers

Improve the coherence of the following paragraph by adding transitions in the blank spaces. Use the italicised hint in brackets to help you choose an apporpriate transition for each blank. If you need to, review the list of Transitions (Logical Organisers)   before you start.

Using transitions

Choose the most appropriate transition from the options given to complete the article:

Overusing transitions

While the use of appropriate transitions can improve coherence (as the previous practice activity shows), it can also be counterproductive if transitions are overused. Use transitions carefully to enhance and clarify the logical connection between ideas in extended texts. Write a range of sentences and vary sentence openings. 

Study the following examples:

Identifying cohesive devices

What is Coherence?

Coherence in writing refers to the logical connections and consistency that hold a text together, making it understandable and meaningful to the reader. Writers create coherence in three ways:

• logical consistency
• conceptual consistency
• linguistic consistency.

What is Logical Consistency?

• For instance, if they argue, “If it rains, the ground gets wet,” and later state, “It’s raining but the ground isn’t wet,” without additional explanation, this represents a logical inconsistency.

What is Conceptual Consistency?

• For example, if you are writing an essay arguing that regular exercise has multiple benefits for mental health, each paragraph should introduce and discuss a different benefit of exercise, all contributing to your main argument. Including a paragraph discussing the nutritional value of various foods, while interesting, would break the conceptual consistency, as it doesn’t directly relate to the benefits of exercise for mental health.

What is Linguistic Consistency?

• For example, if a writer jumps erratically between different tenses or switches point of view without clear demarcation, the reader might find it hard to follow the narrative, leading to a lack of linguistic coherence.

Related Concepts: Flow ; Given to New Contract ; Grammar ; Organization ; Organizational Structures ; Organizational Patterns ; Sentence Errors

Why Does Coherence Matter?

Coherence is crucial in writing as it ensures that the text is understandable and that the ideas flow logically from one to the next. When writing is coherent, readers can easily follow the progression of ideas, making the content more engaging and easier to comprehend. Coherence connects the dots for the reader, linking concepts, arguments, and details in a clear, logical manner.

Without coherence, even the most interesting or groundbreaking ideas can become muddled and lose their impact. A coherent piece of writing keeps the reader’s attention, demonstrates the writer’s control over their subject matter, and can effectively persuade, inform, or entertain. Thus, coherence contributes significantly to the effectiveness of writing in achieving its intended purpose.

How Do Writers Create Coherence in Writing?

• Your thesis statement serves as the guiding star of your paper. It sets the direction and focus, ensuring all subsequent points relate back to this central idea.
• Acknowledge and address potential counterarguments to strengthen your position and add depth to your writing.
• Use the genres and organizational patterns appropriate for your rhetorical situation . A deductive structure (general to specific) is often effective, guiding the reader logically through your argument. Yet different disciplines may privilege more inductive approaches , such as law and philosophy.
• When following a given-to-new order, writers move from what the reader already knows to new information. In formal or persuasive contexts, writers are careful to vet new information for the reader following information literacy laws and conventions .
• Strategic repetition of crucial terms and your thesis helps your readers follow your main ideas and evidence for claims 
• While repetition is useful, varying language with synonyms can prevent redundancy and keep the reader engaged.
• Parallelism in sentences can provide rhythm and clarity, making complex ideas easier to follow.
• Consistent use of pronouns avoids confusion and helps in maintaining a clear line of thought.
• Arrange your ideas in a sequence that naturally builds from one point to the next, ensuring each paragraph flows smoothly into the next .
• Signposting , or using phrases that indicate what’s coming next or what just happened, can help orient the reader within your argument.
• Don’t bother repeating your argument in your conclusion. Prioritize conciseness. Yet end with a call to action or appeal to kairos and ethos .

Recommended Resources

• Organization
• Organizational Patterns

Brevity - Say More with Less

Clarity (in Speech and Writing)

Coherence - How to Achieve Coherence in Writing

Flow - How to Create Flow in Writing

Inclusivity - Inclusive Language

The Elements of Style - The DNA of Powerful Writing

Suggested Edits

• Please select the purpose of your message. * - Corrections, Typos, or Edits Technical Support/Problems using the site Advertising with Writing Commons Copyright Issues I am contacting you about something else
• Your full name
• Your email address *
• Page URL needing edits *
• Phone This field is for validation purposes and should be left unchanged.

Other Topics:

Citation - Definition - Introduction to Citation in Academic & Professional Writing

• Joseph M. Moxley

Explore the different ways to cite sources in academic and professional writing, including in-text (Parenthetical), numerical, and note citations.

Collaboration - What is the Role of Collaboration in Academic & Professional Writing?

Collaboration refers to the act of working with others or AI to solve problems, coauthor texts, and develop products and services. Collaboration is a highly prized workplace competency in academic...

Genre may reference a type of writing, art, or musical composition; socially-agreed upon expectations about how writers and speakers should respond to particular rhetorical situations; the cultural values; the epistemological assumptions...

Grammar refers to the rules that inform how people and discourse communities use language (e.g., written or spoken English, body language, or visual language) to communicate. Learn about the rhetorical...

Information Literacy - Discerning Quality Information from Noise

Information Literacy refers to the competencies associated with locating, evaluating, using, and archiving information. In order to thrive, much less survive in a global information economy — an economy where information functions as a...

Mindset refers to a person or community’s way of feeling, thinking, and acting about a topic. The mindsets you hold, consciously or subconsciously, shape how you feel, think, and act–and...

Rhetoric: Exploring Its Definition and Impact on Modern Communication

Learn about rhetoric and rhetorical practices (e.g., rhetorical analysis, rhetorical reasoning,  rhetorical situation, and rhetorical stance) so that you can strategically manage how you compose and subsequently produce a text...

Style, most simply, refers to how you say something as opposed to what you say. The style of your writing matters because audiences are unlikely to read your work or...

The Writing Process - Research on Composing

The writing process refers to everything you do in order to complete a writing project. Over the last six decades, researchers have studied and theorized about how writers go about...

Writing Studies

Writing studies refers to an interdisciplinary community of scholars and researchers who study writing. Writing studies also refers to an academic, interdisciplinary discipline – a subject of study. Students in...

Featured Articles

Academic Writing – How to Write for the Academic Community

Professional Writing – How to Write for the Professional World

Credibility & Authority – How to Be Credible & Authoritative in Speech & Writing

Cohesion And Coherence In Essay Writing

Table of contents, introduction.

Coherent essays are identified by relevance to the central topic. They communicate a meaningful message to a specific audience and maintain pertinence to the main focus. In a coherent essay, the sentences and ideas flow smoothly and, as a result, the reader can follow the ideas developed without any issues.

To achieve coherence in an essay, writers use lexical and grammatical cohesive devices. Examples of these cohesive devices are repetition, synonymy, antonymy, meronymy, substitutions , and anaphoric or cataphoric relations between sentences. We will discuss these devices in more detail below.

This article will discuss how to write a coherent essay. We will be focusing on the five major points.

• We will start with definitions of coherence and cohesion.
• Then, we will give examples of how a text can achieve cohesion.
• We will see how a text can be cohesive but not coherent.
• The structure of a coherent essay will also be discussed.
• Finally, we will look in detail at ways to improve cohesion and write a coherent essay.

Before illustrating how to write coherent essays, let us start with the definitions of coherence and cohesion and list the ways we can achieve cohesion in a coherent text.

Definitions Cohesion and Coherence

In general, coherence and cohesion refer to how a text is structured so that the elements it is constituted of can stick together and contribute to a meaningful whole. In coherent essays, writers use grammatical and lexical cohesive techniques so that ideas can flow meaningfully and logically.

What is coherence?

Coherence refers to the quality of forming a unified consistent whole. We can describe a text as being coherent if it is semantically meaningful, that is if the ideas flow logically to produce an understandable entity.

If a text is coherent it is logically ordered and connected. It is clear, consistent, and understandable.

Coherence is related to the macro-level features of a text which enable it to have a sense as a whole.

What is cohesion?

Cohesion is commonly defined as the grammatical and lexical connections that tie a text together, contributing to its meaning (i.e. coherence.)

While coherence is related to the macro-level features of a text, cohesion is concerned with its micro-level – the words, the phrases, and the sentences and how they are connected to form a whole.

If the elements of a text are cohesive, they are united and work together or fit well together.

To summarize, coherence refers to how the ideas of the text flow logically and make a text semantically meaningful as a whole. Cohesion is what makes the elements (e.g. the words, phrases, clauses, and sentences) of a text stick together to form a whole.

How to Achieve Cohesion And Coherence In Essay Writing

There are two types of cohesion: lexical and grammatical. Writers connect sentences and ideas in their essays using both lexical and grammatical cohesive devices.

Lexical cohesion

We can achieve cohesion through lexical cohesion by using these techniques:

• Repetition.

Now let’s look at these in more detail.

Repeating words may contribute to cohesion. Repetition creates cohesive ties within the text.

• Birds are beautiful. I like birds.

You can use a word or phrase that means exactly or nearly the same as another word to achieve cohesion.

• Paul saw a snake under the mattress. The serpent was probably hiding there for a long time.

Antonymy refers to the use of a word of opposite meaning. This is often used to create links between the elements of a text.

• Old movies are boring, the new ones are much better.

This refers to the use of a word that denotes a subcategory of a more general class.

• I saw a cat . The animal was very hungry and looked ill.

Relating a superordinate term (i.e. animal) to a corresponding subordinate term (i.e. cat) may create more cohesiveness between sentences and clauses.

Meronymy is another way to achieve cohesion. It refers to the use of a word that denotes part of something but which is used to refer to the whole of it for instance faces can be used to refer to people as in “I see many faces here”. In the following example, hands refer to workers.

• More workers are needed. We need more hands to finish the work.

Grammatical cohesion

Grammatical cohesion refers to the grammatical relations between text elements. This includes the use of:

• Cataphora .
• Substitutions.
• Conjunctions and transition words.

Let us illustrate the above devices with some examples.

Anaphora is when you use a word referring back to another word used earlier in a text or conversation.

• Jane was brilliant. She got the best score.

The pronoun “she” refers back to the proper noun “Jane”.

Cataphora is the opposite of anaphora. Cataphora refers to the use of a word or phrase that refers to or stands for a following word or phrase.

• Here he comes our hero. Please, welcome John .

The pronoun “he” refers back to the proper noun “John”.

Ellipsis refers to the omission from speech or writing of a word or words that are superfluous or able to be understood from contextual clues.

• Liz had some chocolate bars, and Nancy an ice cream.

In the above example, “had” in “Nancy an ice cream” is left because it can be understood (or presupposed) as it was already mentioned previously in the sentence.

Elliptic elements can be also understood from the context as in:

• A: Where are you going?

Substitutions

Substitutions refer to the use of a word to replace another word.

• A: Which T-shirt would you like?
• B: I would like the pink one .

Conjunctions transition words

Conjunctions and transition words are parts of speech that connect words, phrases, clauses, or sentences.

• Examples of conjunctions: but, or, and, although, in spite of, because,
• Examples of transition words: however, similarly, likewise, specifically, consequently, for this reason, in contrast to, accordingly, in essence, chiefly, finally.

Here are some examples:

• I called Tracy and John.
• He was tired but happy.
• She likes neither chocolates nor cookies.
• You can either finish the work or ask someone to do it for you.
• He went to bed after he had done his homework.
• Although she is very rich, she isn’t happy.
• I was brought up to be responsible. Similarly , I will try to teach my kids how to take responsibility for their actions.

Cohesive but not coherent texts

Sometimes, a text may be cohesively connected, yet may still be incoherent.

Learners may wrongly think that simply linking sentences together will lead to a coherent text.

Here is an example of a text in which sentences are cohesively connected, yet the overall coherence is lacking:

The player threw the ball toward the goalkeeper. Balls are used in many sports. Most balls are spheres, but American football is an ellipsoid. Fortunately, the goalkeeper jumped to catch the ball. The crossbar in the soccer game is made of iron. The goalkeeper was standing there.

The sentences and phrases in the above text are decidedly cohesive but not coherent.

There is a use of:

• Repetition of: the ball, goalkeeper, the crossbar.
• Conjunctions and transition words: but, fortunately.

The use of the above cohesive devices does not result in a meaningful and unified whole. This is because the writer presents material that is unrelated to the topic. Why should a writer talk about what the crossbar is made of? And is talking about the form balls in sports relevant in this context? What is the central focus of the text?

A coherent essay has to be cohesively connected and logically expressive of the central topic.

How to write a coherent essay?

1. start with an outline.

An outline is the general plan of your essays. It contains the ideas you will include in each paragraph and the sequence in which these ideas will be mentioned.

It is important to have an outline before starting to write. Spending a few minutes on the outline can be rewarding. An outline will organize your ideas and the end product can be much more coherent.

Here is how you can outline your writing so that you can produce a coherent essay:

• Start with the thesis statement – the sentence that summarizes the topic of your writing.
• Brainstorm the topic for a few minutes. Write down all the ideas related to the topic.
• Sift the ideas brainstormed in the previous step to identify only the ideas worth including in your essay.
• Organize ideas in a logical order so that your essay reflects the unified content that you want to communicate.
• Each idea has to be treated in a separate paragraph.
• Think of appropriate transitions between the different ideas.
• Under each idea/paragraph, write down enough details to support your idea.

After identifying and organizing your ideas into different paragraphs, they have to fit within the conventional structure of essays.

2. Structure your essay

It is also important to structure your essay so that you the reader can identify the organization of the different parts of your essay and how each paragraph leads to the next one.

Here is a structure of an essay

3. Structure your paragraphs

Paragraphs have to be well-organized. The structure of each paragraph should have:

• A topic sentence that is usually placed at the beginning,
• Supporting details that give further explanation of the topic sentence,
• And a concluding sentence that wraps up the content of the paragraph.

The supporting sentences in each paragraph must flow smoothly and logically to support the purpose of the topic sentence. Similarly, each paragraph has to serve the thesis statement, the main topic of the essay.

4. Relevance to the main topic

No matter how long the essay is, we should make sure that we stick to the topic we want to talk about. Coherence is about making everything flow smoothly to create unity. So, sentences and ideas must be relevant to the central thesis statement.

The writer has to maintain the flow of ideas to serve the main focus of the essay.

5. Stick to the purpose of the type of essay you’re-writing

Essays must be clear and serve a purpose and direction. This means that the writer’s thoughts must not go astray in developing the purpose of the essay.

Essays are of different types and have different purposes. Accordingly, students have to stick to the main purpose of each genre of writing.

• An expository essay aims to inform, describe, or explain a topic, using essential facts to teach the reader about a topic.
• A descriptive essay intends to transmit a detailed description of a person, event, experience, or object. The aim is to make the reader perceive what is being described.
• A narrative essay attempts to tell a story that has a purpose. Writers use storytelling techniques to communicate an experience or an event.
• In argumentative essays, writers present an objective analysis of the different arguments about a topic and provide an opinion or a conclusion of positive or negative implications. The aim is to persuade the reader of your point.

6. Use cohesive devices and signposting phrases

Sentences should be connected using appropriate cohesive devices as discussed above:

Cohesive devices such as conjunctions and transition words are essential in providing clarity to your essay. But we can add another layer of clarity to guide the reader throughout the essay by using signpost signals.

What is signposting in writing?

Signposting refers to the use of phrases or words that guide readers to understand the direction of your essay. An essay should take the reader on a journey throughout the argumentation or discussion. In that journey, the paragraphs are milestones. Using signpost signals assists the reader in identifying where you want to guide them. Signposts serve to predict what will happen, remind readers of where they are at important stages along the process, and show the direction of your essay.

Essay signposting phrases

The following are some phrases you can use to signpost your writing:

It should be noted though that using cohesive devices or signposting language may not automatically lead to a coherent text. Some texts can be highly cohesive but remain incoherent. Appropriate cohesion and signposting are essential to coherence but they are not enough. To be coherent, an essay has to follow, in addition to using appropriate cohesive devices, all the tips presented in this article.

7. Draft, revise, and edit

After preparing the ground for the essay, students produce their first draft. This is the first version of the essay. Other subsequent steps are required.

The next step is to revise the first draft to rearrange, add, or remove paragraphs, ideas, sentences, or words.

The questions that must be addressed are the following:

• Is the essay clear? Is it meaningful? Does it serve the thesis statement (the main topic)?
• Are there sufficient details to convey ideas?
• Are there any off-topic ideas that you have to do without?
• Have you included too much information? Does your writing stray off-topic?
• Do the ideas flow in a logical order?
• Have you used appropriate cohesive devices and transition words when needed?

Once the revision is done, it is high time for the editing stage. Editing involves proofreading and correcting mistakes in grammar and mechanics. Pay attention to:

• Verb tense.
• Subject-verb agreement.
• Sentence structure. Have you included a subject a verb and an object (if the verb is transitive.)
• Punctuation.
• Capitalization.

Coherent essays are identified by relevance to the thesis statement. The ideas and sentences of coherent essays flow smoothly. One can follow the ideas discussed without any problems. Lexical and grammatical cohesive devices are used to achieve coherence. However, these devices are not sufficient. To maintain relevance to the main focus of the text, there is a need for a whole process of collecting ideas, outlining, reviewing, and editing to create a coherent whole.

More writing lessons are here .

Related Pages:

• Figures of speech
• Articles about writing

Enhance Academic Writing with Coherence Techniques

Learn strategies to improve the clarity, coherence, and cohesion of your academic writing for a more persuasive and impactful paper. Discover techniques that facilitate effective communication in academia.

Lennart Nacke

In today’s newsletter, we'll be discussing how to make your writing more coherent. As you know, effective communication is crucial to success in academia. Writing with clarity and coherence is crucial to convey ideas and research effectively to readers. One of the most important skills for writers is to make their writing coherent. Cohesion helps readers understand complex ideas and arguments, and facilitates the construction of rigorous and nuanced arguments. Therefore, producing work that is both persuasive and impactful is vital to succeed in academic writing.

However, many writers struggle with coherence in their writing. Their writing suffers from unclear connections between ideas, poor organization, and a lack of cohesion within paragraphs. These issues can cause confusion and frustration for readers, making it difficult for the writer to get their point across and potentially decreasing the effectiveness of their work. However, there are techniques and practices that can be used to overcome these issues. This newsletter issue provides some strategies and methods that help you create writing that is clear and interesting.

A well-written paper ties together different ideas and concepts in a way that makes sense. Cohesion and coherence are achieved by linking ideas well within and between paragraphs, reiterating the main point, and, if necessary, giving detailed explanations. Transitions are the most obvious way to show how different ideas are related to each other, but there are other ways and examples that writers can use to do the same thing.

So, before I dive into transitions below with a great list of examples that you can use right away, let’s discuss some other ways to make your writing more coherent.

Parallelism

Parallel sentence structure is a technique that can make academic writing easier for readers to follow. You essentially repeat similar phrase structures, which may help generate a feeling of rhythm and balance in your paper. By adopting parallel sentence patterns, you are able to emphasize significant ideas and facilitate the comprehension of difficult issues.

Consider using parallel sentence structures as you write your paper to create a sense of progression in your ideas. For example, you can use parallel sentence structures to describe different aspects of a complex concept, such as "The first aspect of X is..., the second aspect of X is..., and the third aspect of X is...". This breaks down the concept into manageable parts and makes it easier for readers to understand.

Another way to use this technique is to emphasize key points or arguments. For instance, you might use parallel sentence structures to describe different examples that support your argument, such as "Example A demonstrates..., Example B illustrates..., and Example C confirms...". This can help reinforce your argument and make it more persuasive for the reader.

Parallel sentence patterns support readers in digesting complicated ideas and following the progression of ideas. They also create a feeling of rhythm and balance in your writing, emphasizing key themes in within your paper structure.

Repetition of essential words, ideas, or phrases is an effective method that can strengthen the structure of an academic paper and make it simpler for the reader to follow the argument's major themes. By consistently using these core phrases throughout the document, authors can establish coherence and unity that reinforce the overall message.

For instance, when you are discussing a complex concept like "postmodernism," you may use this term repeatedly throughout the text to help readers understand the main idea. You may also use specific terms and phrases like "postmodern philosophy" or "postmodern literature" to emphasize this concept and make the paper easier to comprehend.

This strategy may also be used by repeating significant ideas or arguments throughout the document. You may, for instance, offer an important idea in the opening and then repeat it in the conclusion to underline its significance and make a lasting impact on the reader. This may also assist to generate a feeling of cohesion and unity throughout the various sections of the article.

Synonyms are a way to improve the flow of academic writing by varying word choice. Synonyms are words that have the same or a similar meaning. They can prevent repetition in a document while maintaining emphasis. This method can also add variety to a document, making it more interesting for the reader.

You have to be careful though with core concepts in your paper, those should be used consistently throughout for clarity. Repeat your key words for clarity (but do not do this excessively). So, for anything directly relevant to your experiment or theory, I do not advise switching out synonyms because this will just confuse your reviewers. However, when it comes to words that clarify important ideas, feel free to use different terms to add diversity to your paper.

Lexical Chains

Lexical chains are sequences of related words that illustrate the consistency and relevance of vocabulary choices to the paper's topic. Using lexical chains creates variety in writing and prevents monotony. For your paper, consider using synonyms, antonyms, hyponyms, superordinates, and other related words to create lexical chains that maintain coherence within a paragraph or section.

For instance, when discussing user interface design, you could use a lexical chain such as "usability - user experience - user satisfaction - user engagement - user loyalty" to maintain coherence and emphasize the significance of these related concepts. Similarly, when discussing artificial intelligence and machine learning, you might use a lexical chain like "data processing - algorithms - learning models - neural networks - deep learning" to illustrate the various stages of the machine learning process and ensure coherence in your discussion.

Lexical chains create coherent flow in your academic paper, making it easier for readers to follow your argument and understand the relevance of your vocabulary choices to the topic at hand.

Topic Sentences

Topic sentences establish order in your writing. You begin each paragraph with a clear and succinct topic sentence. This assists your readers comprehend the paragraph's key point and its relationship to your main research question or hypothesis. Consider using topic sentences to establish a focused framework that takes the reader through your argument as you compose your paper.

For instance, if you are addressing the design principles of user interfaces, you may begin a paragraph with a subject phrase such as "The concept of simplicity is an important factor in user interface design." Afterwards, you may add supporting data and specifics to explain how this idea pertains to the design of user interfaces. In the same way, if you are addressing the ethical implications of artificial intelligence, you may begin a paragraph with a subject phrase such as "The potential misapplication of AI creates a huge ethical concern." You might then give supporting information and evidence to show the possible hazards and difficulties involved with the usage of artificial intelligence.

The topic sentence sets the tone for your paragraph. It ties each paragraph back to your overarching question. Start each sentence or paragraph with information that hints at the content of the next sentence. Using this structure in your writing may not only make the entire paper more coherent but help you be more convincing in your argumentation.

Cohesive Nouns

Cohesive nouns, also known as "umbrella nouns," summarize multiple related words or concepts into one term. For example, the cohesive noun "family" encompasses a group of individuals who are related by blood or marriage and who live together in a household unit. By using cohesive nouns, you can reduce wordiness and improve the clarity and flow of your writing by condensing multiple related ideas into a single, broad term.

Using cohesive nouns can help you avoid repetition and create a more sophisticated tone in your writing. For example, when discussing the various components of a user interface, you can use the cohesive noun "interface elements" to refer to buttons, menus, and other related components. This not only avoids repeating the same words multiple times but also creates a more concise and polished sentence. Similarly, when discussing the impact of technology on society, you can use the cohesive noun "digital technologies" to refer to computers, smartphones, and other related technologies.

With cohesive nouns, you can create a more efficient and streamlined paper that avoids unnecessary repetition and keeps the focus on your central argument. Additionally, cohesive nouns can also help to clarify complex topics and make them more accessible to a wider audience by simplifying the language and reducing confusion.

Transitions

Transitions are crucial in academic writing, as they play a vital role in connecting different parts of a paper and making the writer's argument clear and easy to follow. In addition to using simple words (e.g., "however", "therefore", "in addition", "on the other hand") to indicate relationships between sentences or paragraphs, full transition sentences and templates can be extremely helpful in crafting an effective academic paper. These templates provide writers with a clear structure and framework for their writing, helping them to maintain coherence and clarity throughout the paper.

To this end, I have compiled a comprehensive list of transitions that I use in my paper writing. Each of these transitions is the start of an example sentence, providing me with a solid starting point to create structure and better coherence in my writing. By using these transitions, I can help my readers follow the flow of my argument, understand how my ideas are connected, and ultimately create a more convincing and compelling academic paper.

Examples for teasing fresh content:

• This section discusses...
• What follows is a description of...
• Throughout the following pages, I will discuss...
• Below is a concise summary of...
• In the following section, it will be claimed that...
• The next section discusses the issue with X.
• The next section provides a more in-depth description of X.
• The following section will describe X's structure and functionality.
• The subsequent section of this study describes in further detail the...

Examples for introducing novel topics:

• With respects to X, …
• With relation to X,...
• Regarding X, …
• In the instance of X...
• With regard to X,...
• On the subject of X,...
• Concerning X,…

Examples for revisiting a topic:

• As said before,...
• As indicated previously,...
• As previously mentioned,...
• As stated earlier...
• As stated on the preceding page,...
• As discussed in the preceding chapter,...
• Returning briefly to the topic of X,...
• As stated in the introduction, it is obvious that...
• As was mentioned in the paper's introduction,...

Examples for transitioning between paper sections:

• Now, let's discuss...
• Let us now consider...
• Now, let us examine...
• Now we will examine...
• Regarding the experimental data on...
• Before starting to investigate X, it is necessary to...
• Before describing these ideas, it is essential to...
• Having stated the meaning of X, I shall now proceed to explain...
• This study has so far focused on X. The following section will address...
• This chapter has established that... It is now required to describe the progression of...
• After discussing how to create X, the last portion of this work discusses...
• This section examined the causes of X and claimed that... The next section of this paper will...

Examples for contrasting argumentation between sections:

• Another essential/vital/crucial component of X is...
• Also, it is essential to inquire...
• In contrast to Smith, Jones (2014) has suggested...
• Jones (2014) argues, in contrast to Smith,...
• Nevertheless, little progress has been achieved in...
• Yet, this approach has a number of significant limitations.
• Yet, despite these new studies about the function of...,
• Similarly, the research of Y indicates that...
• In comparison to X, Y's findings show...
• However, it is important to note that there are some limitations to Y's approach…
• On the other hand, there is evidence that contradicts this argument…
• While X has been found to be effective in certain contexts, there are limitations to its application in other contexts…
• Despite the criticisms of X, it remains a widely used and important tool in the field of Y…
• While X is often viewed as the standard approach, recent research has challenged this assumption…

Examples for recapping sections:

• Our paper has so far claimed that...
• The preceding section demonstrated that...
• In this section's last sentence, the literature identifies...
• This section has examined the three most important characteristics of...
• This review has shown, in conclusion, that...
• This chapter has detailed the methodologies used in this inquiry and has...
• This section aims to give a concise review of the literature concerning...
• This chapter begins with a description of X and an argument that... It went on to imply that the...
• It has been stated in this section that... The subsequent chapter carries on to analyze the...

Transitions are an essential component of academic writing that aims for clarity and coherence. By using both simple words and longer transition phrases, you can establish connections between sentences and paragraphs and maintain a consistent structure throughout your work. Templates can be especially helpful for those who want to create content for successful academic papers quicker. By providing a framework and structure for writing, templates help maintain consistency and clarity. With this comprehensive list of transitions, you can take the first step in composing a well-structured and convincing academic paper that engages your audience.

To wrap up today’s issue, ensuring coherence in your writing is essential to effectively convey your ideas and research. You can achieve cohesion and coherence in various ways, such as using parallelism, repetition, synonyms, lexical chains, topic sentences, and cohesive nouns. Additionally, transitions are crucial for connecting different parts of your paper and making your argument clear and easy to follow. By using templates and a comprehensive list of transitions, you can establish connections between sentences and paragraphs and maintain a consistent structure throughout your work. Always consider your readers and use these techniques wisely.

Philosophical Disquisitions

Things hid and barr'd from common sense

Monday, October 13, 2014

How can you make your writing more coherent four tips.

It dawned on me that a clearer trajectory through this morass would consist of zooming in [on the brain] from a bird’s-eye view to increasingly microscopic components. From the highest vantage point you can make out only the brain’s two big hemispheres, so I began with studies of split-brain patients and other discoveries that locate language in the left hemisphere. Zooming in on that hemisphere, one can see a big cleft dividing the temporal lobe from the rest of the brain, and the territory on the banks of that cleft repeatedly turns up as crucial for language in studies of stroke patients and brain scans of intact subjects. Moving in closer, one can distinguish various regions — Broca’s area, Wernicke’s area, and so on — and the discussion can turn to the more specific language skills, such as recognising words and parsing them into a tree, that have been tied to each area.  

( Sense of Style, p. 144 )

The procedure is actually quite simple. First you arrange things into different groups depending on their makeup. Of course, one pile may be sufficient depending on how much there is to do. If you have to go somewhere else due to lack of facilities that is the next step, otherwise you are pretty well set. It is important not to overdo any particular endeavour. That is, it is better to do too few things at once than too many. In the short run this may not seem important, but complications from doing too many can easily arise. A mistake can be expensive as well. The manipulation of the appropriate mechanisms should be self-explanatory, and we need not dwell on it here. At first the whole procedure will seem complicated. Soon, however, it will become just another facet of life. It is difficult to foresee any end to the necessity for this task in the immediate future, but then one never can tell.

Similarity : Shows how one idea is similar to another, e.g. “Darwin’s theory of evolution was like that of Alfred Russell Wallace.” A similarity relation is commonly signalled by the use of and , similarly , likewise and too .

Contrast : Shows how one idea differs from another, e.g. “Hobbes conceived of the state of nature as a war of all against all. Rousseau had a much rosier view.” A contrast relation is commonly signalled by the use of but , in contrast , on the other hand , and alternatively .

Elaboration : Describes something in a generic way first, and then in specific detail, e.g. “Justice is about fairness. It is about making sure that everybody gets an equal share of public resources.” Elaboration is commonly signalled by the use of a colon (:), that is , in other words , which is to say , also , furthermore , in addition , notice that , and which .

Exemplification : Starts with a generalisation and then gives one or more examples, e.g. “Free will is a deeply contested concept. There are as many different theories of free will as there are days of the week: agent causalist theories, event-causal libertarianist theories, compatibilist and semi-compatibilist theories, illusionist theories, hard-determinist theories and so on.” Exemplification is commonly signalled by the use of for example , for instance , such as , including and a colon (:).

Generalisation : Starts with a specific example and then gives a general rule, e.g. “There are as many different theories of free will as there are days of the week: agent causalist theories, event-causal libertarianist theories, compatibilist and semi-compatibilist theories, illusionist theories, hard-determinist theories and so on. This shows that free will is a deeply contested concept.” Generalisation is commonly signalled by in general , and more generally .

Exception - exception first : Gives an exception first and then gives the general rule, e.g. “David Hume was good-natured and witty. But philosophers are usually a sour bunch.” This is commonly signalled by however , on the other hand , and then there is .

Exception - generalisation first : Gives the generalisation first and then givse the exception, e.g. “Philosophers are usually a sour bunch. But David Hume was good-natured and witty.” This is commonly signalled by nonetheless , nevertheless , and still .

Sequence - before-and-after: Says that one thing happened and then another thing happened afterwards, e.g. “Darwin went on a five year voyage on the HMS Beagle. He then came home and developed his theory of evolution.” This type of sequence is commonly signalled by and , before , and then .

Sequence - after-and-before: Says that one thing happened and before that another thing, e.g. “Darwin developed his theory of evolution while living in Down House in Kent. Before that he had been a five-year voyage on the HMS Beagle.” This type of sequence is commonly signalled by after , once , while and when .

Result (cause-effect) : Introduces an explanatory principle or rule, then says what follows from that rule, e.g. “David Hume was living in an era of religious intolerance, that’s why he never published his Dialogues Concerning Natural Religion during his lifetime.” This type of relation is commonly signalled by and , as a result , therefore , and so .

Explanation (effect-cause): States what happened first, then introduces the explanation, e.g. “The Soviet Union collapsed in 1991. This was because of internal corruption and decay.” This type of relation is commonly signalled by because , since , and owing to .

Violated expectation (preventer-effect) : Used when the cause prevents something from happening that would otherwise have happened, e.g. “Darwin would never have published his theory were it not for Huxley’s intervention.” This is commonly signalled by but , while , however , nonetheless , and yet .

Failed prevention (effect preventer) : Used when the cause fails to prevent something from happening, e.g. “Darwin published his theory, despite his concerns about the religious backlash.” This is commonly signalled by despite and even though .

Attribution : Used when you want to attribute an idea or action or belief (or whatever) to a particular agent or individual, e.g. “Hume thought that there was no logical connection between the fact that the sun rose yesterday, and the fact that it would rise again tomorrow.” This is commonly signalled by according to , or X stated that .

1. Adopt a sensible overarching structure: Make your point in a logical, easy-to-follow fashion. Adopting, spatial or temporal metaphors can help you to do this, e.g. imagining your argument as something with a visible structure.

2. Introduce the reader to the topic and the point: Make sure they know what you are talking about and why you are talking about it.

3. Help the reader keep track of the protagonists : Always be mindful of the person, concept or argument you are discussing. Make sure you keep the reader focused on that person, concept or argument. Avoid elegant variation.

4. Understand how coherence relations work : Be aware of how the ideas, concepts, agents, or events you are discussing relate to one another. Make sure the reader can follow those relations, either explicitly (through connective phrases) or implicitly (by good paragraph and sentence structuring).

2 comments:

This comment has been removed by the author.

• My Playlists
• Media Upload

• College of Agriculture
• Anthropology Forums
• Geography and Planning
• Health and Community Services
• Interdisciplinary Center on Aging
• Multicultural and Gender Studies
• Political Science
• School of Social Work
• Chico State Center for Entrepreneurship
• Student Success Center
• Journalism & Public Relations
• School of Education
• Executive Lecture Series
• Art & Art History
• Humanities Center
• International Forums
• Languages and Cultures
• Middle East Studies Symposium
• Religious Studies
• The Peace Institute at CSU_ Chico
• The Writers Voice
• Biological Sciences Academic Forums
• Chemistry and Biochemistry
• Data Science Certificate
• Geological & Environmental Sciences
• Mathematics and Statistics Colloquiums
• Black Lives Matter Teach-In
• Center for Water and the Environment
• Cross-Cultural Leadership Center Passing on the Legacy Series
• Diversity (Conversations on Diversity & Inclusion)
• Gateway Science Museum
• PATH Scholars
• Women in Science
• Book in Common
• Commencement
• Boys Adrift & Girls on the Edge
• Indigenous Earth Relations Alliance
• Institute for Sustainable Development
• LEAD Dreaming without Boundaries
• Women Like You Leadership Symposium
• Academic Advising Programs Workshops
• Career Center Workshops
• Cross-Cultural Leadership Center Workshops
• Graduate Studies Masters Thesis/Project Writing Workshops
• Liberal Studies CSET Exam Workshops
• Student Learning Center Workshops
• Student Life and Leadership
• California Health Professional Student Alliance
• Computer Graphics Club
• STOP Human Trafficking
• Summer Orientation Workshops
• Wellness Center Workshops
• Wildcat Leadership Institute

How to Write a Unified, Coherent Essay

Related media.

Please log in to save materials. Log in

• Resource Library
• English Composition
• OERizona Network
• Writer's Handbook

Crafting Coherent Essays MSWord

Crafting coherent essays pdf, crafting coherent essays: an academic writer's handbook.

This OER textbook has been developed to support English Composition courses at rural Arizona community colleges. It includes content to help students develop and assemble academic essays.

ABOUT THIS BOOK

This book, which can be accessed by downloading the attached Microsoft Word document or PDF, has been developed to support English Composition courses at rural Arizona community colleges. It includes content to help students develop and assemble academic essays.

This book has been assembled by Erik Wilbur, John Hansen, and Beau Rogers at Mohave Community College.

Most of the content in this book has been sourced from creative commons licensed materials. Attributions for this borrowed and/or adapted and remixed content can be found at the end of each chapter.

Any text, graphic, or video without an attribution should be attributed to Mohave Community College by using the license below.

Crafting Coherent Essays: An Academic Writer’s Handbook by Mohave Community College is licensed under a Creative Commons Attribution License except where otherwise noted.

Version History

Pasco-Hernando State College

• Unity and Coherence in Essays
• The Writing Process
• Paragraphs and Essays
• Proving the Thesis/Critical Thinking
• Appropriate Language

Test Yourself

• Essay Organization Quiz
• Sample Essay - Fairies
• Sample Essay - Modern Technology

Related Pages

• Proving the Thesis

Unity is the idea that all parts of the writing work to achieve the same goal: proving the thesis. Just as the content of a paragraph should focus on a topic sentence, the content of an essay must focus on the thesis.  The introduction paragraph introduces the thesis, the body paragraphs each have a proof point (topic sentence) with content that proves the thesis, and the concluding paragraph sums up the proof and restates the thesis. Extraneous information in any part of the essay which is not related to the thesis is distracting and takes away from the strength of proving the thesis.

An essay must have coherence. The sentences must flow smoothly and logically from one to the next as they support the purpose of  each paragraph in proving the thesis. .

Just as the last sentence in a paragraph must connect back to the topic sentence of the paragraph, the last paragraph of the essay should connect back to the thesis by reviewing the proof and restating the thesis.

Example of Essay with Problems of Unity and Coherence

Here is an example of a brief essay that includes a paragraph that  does not  support the thesis “Many people are changing their diets to be healthier.”

     People are concerned about pesticides, steroids, and antibiotics in the food they eat.  Many now shop for organic foods since they don’t have the pesticides used in conventionally grown food.  Meat from chicken and cows that are not given steroids or antibiotics are gaining in popularity even though they are much more expensive. More and more, people are eliminating pesticides, steroids, and antibiotics from their diets.

    Eating healthier also is beneficial to the environment since there are less pesticides poisoning the earth. Pesticides getting into the waterways is creating a problem with drinking water.  Historically, safe drinking water has been a problem. It is believed the Ancient Egyptians drank beer since the water was not safe to drink.  Brewing beer killed the harmful organisms and bacteria in the water from the Nile.

     There is a growing concern about eating genetically modified foods, and people are opting for non-GMO diets.  Some people say there are more allergic reactions and other health problems resulting from these foods.  Others are concerned because there are no long-term studies which clearly show no adverse health effects such as cancers or other illnesses. Avoiding GMO food is another way people are eating healthier food.

See how just one paragraph  can take away from the effectiveness of the essay in showing how people are changing to healthier food since the unity and coherence are affected.  There is no longer unity among all the paragraphs.  The thought pattern is disjointed and the essay loses its coherence.

Transitions and Logical Flow of Ideas

Transitions are words, groups of words, or sentences that connect one sentence to another or one paragraph to another.

They promote a logical flow from one idea to the next and overall unity and coherence.

While transitions are not needed in every sentence or at the end of every paragraph, they are missed when they are omitted since the flow of thoughts becomes disjointed or even confusing.

There are different types of transitions:

Time – before, after, during, in the meantime, nowadays

Space – over, around, under

Examples – for instance, one example is

Comparison – on the other hand, the opposing view

Consequence – as a result, subsequently

These are just a few examples.  The idea is to paint a clear, logical connection between sentences and between paragraphs.

• Printer-friendly version

SAT Essay: Developing a Coherent and Persuasive Argument

The SAT Essay is an integral component of the SAT exam, designed to evaluate a student's ability to analyze and construct a persuasive argument within a limited timeframe. In this comprehensive guide, we will explore strategies for developing a coherent and persuasive argument that can help you achieve success in this crucial section of the SAT.

 Understanding the SAT Essay Prompt

The SAT Essay presents students with a passage to analyze and respond to within 50 minutes. The passage typically revolves around a thought-provoking topic or issue and may include persuasive elements such as logical reasoning, evidence, and rhetorical devices. Students are tasked with analyzing the author's argument and crafting a persuasive response that demonstrates their ability to evaluate complex ideas and communicate effectively.

 Key Components of a Coherent and Persuasive Argument

1. thesis statement: establishing your position.

      Begin your essay with a clear and concise thesis statement that outlines your stance on the author's argument. Your thesis should encapsulate the main idea of your response and provide a roadmap for the reader to follow.

2. Analysis of Persuasive Elements: Uncovering the Author's Techniques

      Dive into the passage and identify persuasive elements such as rhetorical devices, evidence, and logical reasoning. Analyze how these elements contribute to the author's argument and evaluate their effectiveness in persuading the audience.

3. Development of Supporting Points: Strengthening Your Argument   

   Develop your argument by providing specific examples and evidence to support your thesis. Use relevant quotations, statistics, or anecdotes from the passage to bolster your analysis and strengthen your position.

4. Logical Organization: Structuring Your Essay Effectively   

   Organize your essay in a logical manner, with each paragraph focusing on a specific aspect of your argument. Use transitions to guide the reader smoothly from one point to the next, ensuring coherence and clarity throughout your essay.

5. Counterarguments and Rebuttal: Addressing Potential Objections

   Anticipate potential counterarguments to your thesis and address them proactively. Acknowledge opposing viewpoints and provide reasoned rebuttals to demonstrate the strength of your argument.

6. Conclusion: Reinforcing Your Argument's Impact   

   Summarize your main points and restate your thesis in the conclusion. End with a strong closing statement that reinforces the persuasiveness of your argument and leaves a lasting impression on the reader.

 Tips for Effective Writing

- Time Management: Allocate your time wisely, leaving ample time for planning, writing, and revising your essay within the 50-minute time limit. 

- Clarity and Conciseness: Ensure that your writing is clear, concise, and free of grammatical errors. Use precise language and avoid unnecessary repetition or verbosity.  

- Engagement with the Passage: Engage critically with the passage, carefully analyzing the author's argument and identifying key persuasive elements.

- Use of Examples: Incorporate relevant examples and evidence from the passage to support your analysis and strengthen your argument. 

- Revision and Proofreading: Take the time to revise and proofread your essay before submitting it. Check for errors in grammar, punctuation, and spelling, and ensure that your essay flows smoothly and cohesively.

 Sample Essay Structure

Introduction:.

   - Introduce the passage and provide a brief overview of the author's argument.

   - Present your thesis statement, outlining your position on the argument and previewing the main points of your response.

Body Paragraphs:

1. Analysis of Persuasive Elements:

   - Identify rhetorical devices, evidence, and logical reasoning used by the author.

   - Analyze the effectiveness of these elements in persuading the audience.

2. Development of Supporting Points:

   - Provide specific examples and evidence from the passage to support your analysis.

   - Explain how these examples strengthen your argument and contribute to its persuasiveness.

3. Counterarguments and Rebuttal:

   - Anticipate potential counterarguments to your thesis.

   - Provide reasoned rebuttals to address these objections and reinforce the strength of your argument.

Conclusion:

   - Summarize your main points and restate your thesis.

   - End with a strong closing statement that reinforces the persuasiveness of your argument.

 Conclusion

Crafting a coherent and persuasive argument for the SAT Essay requires critical thinking, analytical skills, and effective communication. By understanding the key components of a strong essay and employing strategic writing techniques, you can develop the proficiency needed to excel in this section of the exam. Approach each prompt with confidence and clarity, and demonstrate your ability to engage critically with complex ideas and communicate them persuasively. With practice and dedication, you can master the art of crafting compelling arguments that leave a lasting impression on the reader.

You Might Also Like

Apply for Federal Grants for College Education

Federal Grants are the popular sources of funding for higher education. If you are unaware of federal grants for college and how it works, then read our blog.

How to Ace Your Common App

Why common application play important role in college admission procedure? Get details about common application and how to create common app account - Read more

How can Conducting Research get you into Your Dream College

Want to get admission in your dream college? Do formal research for college admission that will help you to gain admission in your dream college - Read a blog

Free Resources

The importance of coherence and cohesion in IELTS writing

Coherence and cohesion are two important elements of effective writing, and they are particularly important for success on the IELTS exam. Coherence refers to the logical flow and organization of your writing, while cohesion refers to the way in which your ideas are connected and linked together. In this blog post, we'll discuss the importance of coherence and cohesion in IELTS writing and provide some tips for improving these skills.

1. Coherence

Coherence refers to the logical flow and organization of your writing. A coherent essay is easy to understand and follows a clear and logical structure. It should have a clear introduction, body paragraphs that support the main argument, and a conclusion that summarizes the main points. To improve the coherence of your writing, make sure to:

• Plan your essay before you start writing
• Use headings and subheadings to organize your essay
• Use transitional words and phrases to connect your ideas
• Use proper paragraph structure (topic sentence followed by supporting sentences)

2. Cohesion

Cohesion refers to the way in which your ideas are connected and linked together. A cohesive essay should use language and vocabulary that helps to connect the ideas and create a smooth flow. To improve the cohesion of your writing, make sure to:

• Use a wide range of vocabulary
• Use synonyms to avoid repetition
• Use referencing words (such as "this," "these," "such," "previous," "later," etc.) to link ideas and maintain a sense of continuity throughout your essay
• Use linking words and phrases (such as "however," "in addition," "on the other hand," etc.) to connect your ideas and show the relationship between them

3. Why are coherence and cohesion important for the IELTS exam?

Coherence and cohesion are important for the IELTS exam because they help to make your writing clear, logical, and easy to understand. A well-written essay that is coherent and cohesive will make a better impression on the examiners and increase your chances of success. Additionally, coherence and cohesion are important because they help you communicate your ideas effectively and persuade your reader. By organizing your ideas logically and linking them together effectively, you'll be able to convey your message more effectively and make a stronger argument.

In conclusion, coherence and cohesion are important skills to develop for success on the IELTS exam. By following the tips outlined in this blog post and practicing regularly, you can improve your coherence and cohesion and increase your chances of success on the writing test. With a little bit of effort and attention to detail, you can create a well-written and effective essay that impresses the examiners and helps you reach your academic or professional goals.

• Sample Questions
• Practice Tests
• Test Format
• Paper Based Exam
• Computer Based Exam
• Band Description
• IELTS Scoring
• Group Coaching
• NAATI CCL Coaching
• How to Join Class?
• Login / Signup

Coherence and Cohesion - Important Tips to Ace IELTS Writing Test

One of the key factors for getting success in the IELTS Exam is to know about the test scoring pattern - furthermore, utilizing that knowledge to increase your band score. Understanding the scoring pattern of the test enables you to recognize what the examiners exactly want. You can focus on doing those things and fetch a good score on your IELTS Writing Test.

Get to know one of the most important criteria on which the IELTS Writing Task 2 is scored. That is Coherence & Cohesion .

Coherence & Cohesion holds 25% weightage of the total score in IELTS Writing -Academic and General Training Tests. But, aspirants overlook this criterion most of the time, and this is precisely why they score a low band.

What is Coherence?

Coherence refers to the flow of your writing. It relates to how well you have organized and structured your essay and how well you have logically sequenced your ideas.

When you write an essay, your primary motive is to express your ideas precisely so that the examiner can easily understand your ideas while reading them. Thus, coherence is essential for your Essay Writing task.

Quick Tips to Follow that shows your Coherence Skill in Your Essay

• Separate your Essay with different paragraphs
• Use Linking Phrases to connect paragraphs
• Follow a Logical Sequence with paragraphs

Let's say you are given the following topic to write your essay :

It is becoming increasingly popular to have a year off between completing school and beginning to go to university. What are the advantages and disadvantages of this?

The following essay structure shows your Coherence skills -

• Introduction: Here, you will paraphrase the essay question provided and then present your point of view
• Body Paragraph 1: You can start by discussing one of the advantages
• Body Paragraph 2: Provide another advantage if you want (optional)
• Body Paragraph 3: Discuss one of the disadvantages
• Body Paragraph 4: You can include another disadvantage (optional)
• Conclusion: At this point, you have to summarize all your arguments and restate your point of view

Now that you know what coherence is, you must paraphrase the essay question and maintain a connection between body paragraphs.

For that, you can make use of Linking Phrases . That is, to start with another advantage in Body Paragraph 2, you can use phrases like 'in addition to' or 'furthermore' .

To bridge a logical sequence between advantages and disadvantages, you can begin writing disadvantages using some of the phrases like 'however' , 'on the other hand' or 'let's not forget the fact that' .

Keep these points into consideration while you write your essay for IELTS Writing Test - whether Academic or General Training .

What is Cohesion?

Cohesion means linking words that help you connect ideas. It also relates to how sentences are linked together to form a meaningful paragraph and how paragraphs are linked together to form an essay.

Quick Tips to Follow that shows your Cohesive Skill in your Essay

• Focus only on 1 point in each paragraph to enhance clarity
• Write Complex Sentences with appropriate use of Punctuations
• Link your ideas with Syntax instead of making excessive use of Linking phrases

For your essay to be cohesive, you must ensure that you don't mix up more than one idea in a single paragraph. Instead, you should focus on one particular point, elaborate on it, and it is always recommended to provide a suitable example in support of that point.

Though writing complex sentences is advisable for the IELTS Writing task - Essay, you must use them wisely. There is a fine line between 'Complex sentences' and 'Complicated sentences.' Do make sure you present your thoughts without making the sentences hard to read or understand, in other words, complicated. Furthermore, proper Punctuations can also help you in framing complex sentences.

Complex Sentence: All my friends enjoyed the movie, however, I hardly enjoyed it.

Complicated Sentence: My friends and I went to a movie, but, I did not find it great though they all liked it very much.

Cohesive Devices are used to signify the relationship between different clauses, sentences and paragraphs.

What are Cohesive Devices?

Connectors, linking words, phrases, and transitional words are the Cohesive Devices that you can use in your Essay.

Here are some examples of Cohesive Devices that you can use in your IELTS Writing Task - Essay

• Adding Ideas: 'furthermore', 'in addition to'
• Contrasting Ideas: 'however', 'on the other hand', 'let's not forget the fact that'
• Stating Examples: 'for instance', 'for example', 'to illustrate this'
• Explaining Results: 'consequently', 'as a result'
• Maintaining the Sequence: 'firstly', 'next', 'to begin with'
• Providing Explanations: 'because of', 'due to'
• Concluding Ideas: 'in conclusion', 'to sum up'
• Introducing your Point: 'another point that I would like to add', 'in my opinion'

Note: Never overdo with Cohesive Devices.

You must avoid the use of excessive connectors in a single paragraph. With too many linking words, your writing will not sound natural and fluent. It may also confuse the examiner while reading your essay.

An alternative to prevent unnecessary connectors is to use pronouns and dependent clauses to fulfil your purpose. Use Cohesive Devices Correctly and in Right Quantity.

Here's a Quick Summary to ensure that your Essay Writing Task has these characteristics!

• Start each paragraph with a clear and concise sentence
• Focus only on one main point in each paragraph
• Develop your main idea with proper explanation, evidence, example, or experience
• Use a variety of linking words and phrases
• Make sure that the Cohesive Devices used are appropriate
• Use pronouns and synonyms to avoid repetition
• End your paragraph with a summary sentence that either concludes it or links it to the other paragraph

Coherence and Cohesive play an important part in achieving greater scores in the IELTS Writing test. You can visit IELTS Tutorials to stay updated with the important aspects of IELTS Test. You can start preparing with free online IELTS practice tests and other study materials.

• 500+ Essays, Letters, Graphs & Model Answers
• Plan routine with our Study Planner
• Improve vocabulary with Vocab Bank
• Access 20+ Practice Tests
• App for IELTS Preparation
• Module wise Sample Question

UA Kirovohrad

Recently viewed courses

Recently viewed.

Find Your Dream School

This site uses various technologies, as described in our Privacy Policy, for personalization, measuring website use/performance, and targeted advertising, which may include storing and sharing information about your site visit with third parties. By continuing to use this website you consent to our Privacy Policy and Terms of Use .

   COVID-19 Update: To help students through this crisis, The Princeton Review will continue our "Enroll with Confidence" refund policies. For full details, please click here.

Enter your email to unlock an extra $25 off an SAT or ACT program!

By submitting my email address. i certify that i am 13 years of age or older, agree to recieve marketing email messages from the princeton review, and agree to terms of use., sample act essay prompt (and how to tackle it).

Are you taking the ACT with Writing? No need to stress! The ACT essay follows a predictable format, which means you can practice and prepare beforehand. Take a look at a sample ACT writing prompt and learn five key steps to penning a high-scoring essay.

Keep in mind: The ACT writing essay is optional. Currently, only 27 colleges and universities require the ACT with Writing. You can see the complete list  here . If there is any chance that you might apply to one of those schools, you should register for the ACT with Writing. Not sure where you will apply? You should strongly consider signing up for the essay and keep your options open.

ACT with Writing: Sample Prompt

This example writing prompt comes straight from our book ACT Prep :

Education and the Workplace

Many colleges and universities have cut their humanities departments, and high schools have started to shift their attention much more definitively toward STEM (Science, Technology, Engineering, Mathematics) and away from ELA (English, Language Arts). Representatives from both school boards and government organizations suggest that the move toward STEM is necessary in helping students to participate in a meaningful way in the American workplace. Given the urgency of this debate for the future of education and society as a whole, it is worth examining the potential consequences of this shift in how students are educated in the United States.

Read and carefully consider these perspectives. Each suggests a particular way of thinking about the shift in American education.

Write a unified, coherent essay in which you evaluate multiple perspectives on the issue of how schools should balance STEM and ELA subjects. In your essay, be sure to:

• analyze and evaluate the perspectives given
• state and develop your own perspective on the issue
• explain the relationship between your perspective and those given

Your perspective may be in full agreement with any of the others, in partial agreement, or wholly different. Whatever the case, support your ideas with logical reasoning and detailed, persuasive examples.

How to Write the ACT Essay

Your job is to write an essay in which you take some sort of position on the prompt, all while assessing the three perspectives provided in the boxes. Find a way to anchor your essay with a unique perspective of your own that can be defended and debated, and you are already in the upper echelon of scorers.

Step 1: Work the Prompt

What in the prompt requires you to weigh in? Why is this issue still the subject of debate and not a done deal?

Step 2: Work the Perspectives

Typically, the three perspectives will be split: one for , one against , and one in the middle . Your goal in Step 2 is to figure out where each perspective stands and then identify at least one shortcoming of each perspective. For the example above, ask yourself: 

• What does each perspective consider?
• What does each perspective overlook?

Read More: What's a Good ACT Score?

Step 3: Generate Your Own Perspective

Now it's time to come up with your own perspective! If you merely restate one of the three given perspectives, you won’t be able to get into the highest scoring ranges. You’ll draw from each of the perspectives, and you may side with one of them, but your perspective should have something unique about it.

Step 4: Put It All Together

Now that you have your ideas in order, here's a blueprint for how to organize the ACT essay. This blueprint works no matter what your prompt is.

Step 5: (If There's Time): Proofread

Spend one or two minutes on proofreading your essay if you have time. You’re looking for big, glaring errors. If you find one, erase it completely or cross it out neatly. Though neatness doesn’t necessarily affect your grade, it does make for a happy grader.

Build the right ACT prep plan for you

Our private tutors will help you build a prep plan that's customized to your score goals, study habits, and schedule.

Find a Tutor

Explore Colleges For You

Connect with our featured colleges to find schools that both match your interests and are looking for students like you.

Career Quiz

Take our short quiz to learn which is the right career for you.

Get Started on Athletic Scholarships & Recruiting!

Join athletes who were discovered, recruited & often received scholarships after connecting with NCSA's 42,000 strong network of coaches.

Best 389 Colleges

165,000 students rate everything from their professors to their campus social scene.

SAT Prep Courses

1400+ course, act prep courses, free sat practice test & events,  1-800-2review, free digital sat prep try our self-paced plus program - for free, get a 14 day trial.

Free MCAT Practice Test

I already know my score.

MCAT Self-Paced 14-Day Free Trial

Enrollment Advisor

1-800-2REVIEW (800-273-8439) ext. 1

1-877-LEARN-30

Mon-Fri 9AM-10PM ET

Sat-Sun 9AM-8PM ET

Student Support

1-800-2REVIEW (800-273-8439) ext. 2

Mon-Fri 9AM-9PM ET

Sat-Sun 8:30AM-5PM ET

Partnerships

• Teach or Tutor for Us

College Readiness

International

Advertising

Affiliate/Other

• Enrollment Terms & Conditions
• Accessibility
• Cigna Medical Transparency in Coverage

Register Book

Local Offices: Mon-Fri 9AM-6PM

• SAT Subject Tests

Academic Subjects

• Social Studies

Find the Right College

• College Rankings
• College Advice
• Applying to College
• Financial Aid

School & District Partnerships

• Professional Development
• Advice Articles
• Private Tutoring
• Mobile Apps
• International Offices
• Work for Us
• Affiliate Program
• Partner with Us
• Advertise with Us
• International Partnerships
• Our Guarantees
• Accessibility – Canada

Privacy Policy | CA Privacy Notice | Do Not Sell or Share My Personal Information | Your Opt-Out Rights | Terms of Use | Site Map

©2024 TPR Education IP Holdings, LLC. All Rights Reserved. The Princeton Review is not affiliated with Princeton University

TPR Education, LLC (doing business as “The Princeton Review”) is controlled by Primavera Holdings Limited, a firm owned by Chinese nationals with a principal place of business in Hong Kong, China.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

5 Coherent Paragraphs

When you think about it, there’s no contradiction in the advice of these two American writers. You should respond with genuine feeling and without inhibition to what stimulates you – in our case, a set of texts. But feeling isn’t enough. When Gustave Flaubert asked “Has a drinking song ever been written by a drunken man?” he meant a coherent song. Between “getting it down” and “handing it in” good writers show respect for their readers by organizing their material into recognizable patterns. An important benefit of this is that by distancing yourself from your ideas and putting them in order for your reader, you are forced to shape your own nebulous feelings into clear thoughts.

This brings us to the well-known (but apparently not well enough known) paragraph: the basic unit of composition. The traditional and still useful rule that a paragraph must have unity, coherence, and emphasis only means that it must make sense, that the sentences should fit together smoothly, and that not all the sentences function in the same way.

When you see that its purpose is to support your thesis by developing and connecting your ideas meaningfully, then paragraph structure should appeal to your common sense. As a point of emphasis a topic sentence – whether you choose to put it at the beginning, middle, or end – allows you to control your writing and guide your reader by expressing the main idea of the paragraph. Remember, you’re not writing a mystery novel. There will be relatively few instances in this type of essay when you’ll want to surprise your reader.

Must every paragraph have a topic sentence? Not necessarily: if the main idea is obvious, then a topic sentence may be omitted. But even if it is only implied by your paragraph, you and your reader should be able to state easily the main idea . Whether explicit or implicit, the topic sentence of each of your paragraphs should come out of your thesis statement and lead to your conclusion. Like the paragraph, the whole essay should have unity, coherence, and emphasis. Try this: next time you read an essay, underline only the topic sentences of each paragraph; then reread only what you’ve underlined. In many cases you’ll see that the underlined sentences make up a coherent paragraph all by themselves (this is an easy way to write an abstract, incidentally). That’s because most topic sentences are more specific than the thesis statement that generates them, but still more general than the supporting sentences in the paragraphs that illustrate them. Thus they are transitions between the writer’s promise to the reader and the keeping of that promise.

Examples: Opening Paragraphs

From a student essay discussing Kafka’s The Metamorphosis :

When Nietzsche declared that “God is dead,” he did so with an air of optimism. No longer could man be led about on the tight leash of religion; a man liberated could strive for the status of Overman. But what happens if a man refuses to let go of his “dead” God and remains too fearful to evolve into an Overman? Rejecting the concept of the Christian God means renouncing the scapegoat for the sins of man and accepting responsibility for one’s own actions. In The Metamorphosis Gregor Samsa plays the god-like role of financial provider for his family. However, when his transformation renders him useless in this role, the rest of Samsa’s family undergoes a change of its own: Kafka uses the metamorphoses of both Gregor and his family to illustrate a modern crisis.

Some comments on the structure:   Two provocative introductory sentences, then a transition question and a response that presents the central idea of the essay. Next, introduction of the text and characters under discussion. Finally, the topic sentence of the paragraph, which, as the thesis statement, promises an interpretation. A paragraph such as this engages the reader’s interest right away and makes the reader look forward to the rest of the essay.

From a student essay on the question, “What Do Historians of Childhood Do?”

In his 1982 book The Disappearance of Childhood , Neil Postman argues that the concept of childhood is a recent invention of literate society, enabled by the invention of moveable-type printing. Postman says as a result of television, literate adulthood and preliterate childhood are both vanishing. While Postman’s indictment of TV-culture is provocative, he ignores race, class, ideology, and economic circumstance as factors in the experience of both children and adults. Worse, he ignores history, making sweeping generalizations such as the claim that the pre-modern Greeks had no concept of children. These claims are contradicted by the appearance of children in classical Greek literature and in the Christian Gospels, written in Greek, which admonish their readers to “be as children.” A more useful and much more interesting observation might be that the idea of childhood and the experience of young people has changed significantly since ancient times, and continues to change.

Some comments on the structure:   Like the previous example, this essay begins with a statement from a text (this time with a paraphrase rather than a quotation) and builds towards a thesis statement. In this case the build-up, where the writer disagrees with one of the class texts, is stronger than the thesis. The writer has not stated exactly what he will argue, aside from saying he finds at least some of the ideas of childhood advanced in the course materials unsatisfactory. Keeping the reader in suspense may add to the interest of the essay, but in a short paper it might also waste valuable time and leave the reader unsure whether the writer has really thought things through.

From an essay on Crime and Punishment :

“Freedom depends upon the real…It is as impossible to exercise freedom in an unreal world as it is to jump while you are falling” (Colin Wilson, The Outsider, p. 39). Even without God, modern man is still tempted to create unreal worlds. In Feodor Dostoevsky’s Crime and Punishment Raskolnikov conceives the fantastic theory of the “overman.” After committing murder in an attempt to satisfy his theory, Raskolnikov falls into a delirious, death-like state; then, Lazarus-like, he is raised from the “dead.” His “resurrection” is not, as some critics suggest, a consequence of his love for Sonya and Sonya’s God. Rather, his salvation results from the freedom he gains when he chooses to live without illusions.

Comments: Once more, a stimulating opening. Between the first and last sentences, which frame the paragraph (the last one, as well as being the thesis sentence, is the specific application of the general first sentence), the writer makes her transition to the central idea and introduces the text and character she wishes to discuss. The reader is given enough information to know what to expect. It promises to be an interesting essay.

Each of the writers above chooses to open with a quotation or reference that helps to focus the reader’s attention and reveal the point of view from which a specific interpretation will be made. Movement from the general to the specific is very common in introductory thesis paragraphs, but it is not obligatory. You can begin with your thesis statement as the first sentence; start with a question; or use the entire opening paragraph to set the scene and provide background, then present your thesis in the second paragraph. Make choices and even create new options, so long as your sentences move to create a dominant impression on the reader.

Examples: Middle Paragraphs

From a student essay comparing P’u Sung-ling’s (17th century) The Cricket Boy and Franz Kafka’s (20th century) The Metamorphosis , two stories that deal with a son’s relationship to his family. (The writer’s thesis: according to these authors, one must connect in meaningful ways with other human beings in order to achieve what Virginia Woolf calls “health,” “truth,” and “happiness.”)

The most obvious similarity between Kiti and Gregor is that they both take the forms of insects; however, their and their families’ reactions to the changes account for the essential difference between the characters. Whereas Kiti thinks a cricket represents “all that [is] good and strong and beautiful in the world ( Cricket Boy , p. 2), Gregor is repulsed by his insect body and “closes his eyes so as not to have to see his squirming legs” ( Metamorphosis , p, 3). Their situations also affect their families differently.  Kiti’s experience serves as a catalyst that brings his family closer together: “For the first time, his father had become human, and he loved his father then” ( CB , p. 2). Gregor’s transformation, on the other hand, succeeds in further alienating him from his family: his parents “could not bring themselves to come in to him” ( M , p. 31). While Kiti and his parents develop a bond based on understanding and mutual respect, Gregor becomes not only emotionally estranged from his family, but also physically separated from them.

Some comments on the structure:   The writer is clearly on her way, with specific examples from the texts, to supporting her argument concerning the need for self-respect and communication. Notice that she uses transitions such as “however,” “whereas,” “also,” “on the other hand,” while,” and “not only…, but also…” to connect her thoughts and make her sentences cohere. Transitional words and phrases are the “glue” both within and between paragraphs: they help writers stick to the point, and also allow readers to stay on the path the writer intends.

Transitions

Writers use transitional words and expressions as markers to guide readers on their exploratory journey. They can express relationships very explicitly , which is often exactly what is needed. However, experienced writers can also build more subtle bridges between ideas, hinting at relationships with implicit transitions. These relationships may change from vague impressions to a very concrete statement, as the argument develops, allowing the reader to “discover” the writer’s conclusion as the essay builds to its final paragraph.

Examples of explicit transitional expressions

• Comparison: such as, like, similarly, likewise, in the same way, in comparison, correspondingly, analogous to
• Contrast: but, however, in contrast, although, different from, opposing, another distinction, paradoxically
•   Cause-effect: because, as a result, consequently, for this reason, produced, generated, yielded
•   Sequence: initially, subsequently, at the onset, next, in turn, then, ultimately
•  Emphasis: above all, of major interest, unequivocally, significantly, of great concern, notably
•   Examples: for example, in this instance, specifically, such as, to illustrate, in particular
•   Adding points: as well as, furthermore, also, moreover, in addition, again, besides

If you find that you are overusing explicit connectors and your transitions are beginning to feel mechanical (How many times have you used “furthermore” or “however”?  How many “other hands” do you have?), you can improve the flow of your writing either by changing up the transitional expressions, or by shifting toward more implicit transitions. One technique is, in the first sentence of the new paragraph, refer (either explicitly or implicitly) to material in the preceding paragraph. For example:

When Alcibiades does give his speech, we see that his example is Socrates himself.

While this interpretation still seems reasonable, I was surprised at the difficulty of uncovering useable data in the records of past societies.

This sometimes sickening detail that Dante uses to draw the reader emotionally into the Inferno also stimulates the reader to think about what he or she feels.

The Greek system is much more relaxed; obeisance and respect for the gods is not required, although in most cases it seems to make life easier.

Each of these implicit transition sentences builds on the previous paragraph and calls for support in the new paragraph. Even more subtle (that is, more difficult) would be to make the last sentence of the paragraph indicate the direction the next paragraph will take. If you try this, be careful you do not at the same time change the subject. You do not want to introduce a new idea at the end of a paragraph, and destroy its unity. Since it suggests a change in direction, we see this device used most commonly with thesis sentences at the end of introductory paragraphs, or in transitional paragraphs like the example above.

Other examples of hinges writers use to make connections include pronouns referring back to nouns in the previous paragraphs and synonyms to avoid repetition and overuse of pronouns. A good rule is not to overuse any device.

Concluding Paragraphs

From a student essay on Crime and Punishment :

Raskolnikov finally finds a new life:

Indeed he [is] not consciously reasoning at all; he [can] only feel .  Life [has] taken the place of logic and something quite different must be worked out in his mind. (Epi. II, p. 464)

Thus he ends his suffering by abandoning intelligence and reasoning.  Jean-Jacques Rousseau said that “above all the logic of the head is the feeling of the heart.” Ultimately, Raskolnikov transcends the “logic of the head” by discovering love and freedom.

Some comments on the structure: The paragraph works well as a conclusion because you can tell immediately that the writer has said all that she wants to say about the subject. She uses a quotation from another source, to “rub up against” Dostoevsky, expanding the dialogue between the text, the writer, and the reader by adding another voice. The answer to the “so what?” question is implied in the last sentence: love and freedom are values we all can share. Note that although this is a different conclusion from that of the earlier essay discussing Crime and Punishment , both interpretations are interesting and valid because both writers supported their arguments with careful readings of the text.

From a History essay analyzing the influence of Philippe Ariès’s book Centuries of Childhood on later historians:

In the end, Centuries of Childhood did not establish a conceptual framework for children’s history. Nor did the rival philosophies of history create a new paradigm for children’s history. Ariès identified a subject of study. He was a prospector who uncovered a rich vein of material. Subsequent miners should use whatever tools and techniques are best suited to getting the ore out of the ground. Historians should stop fighting over theories and get to work uncovering the lives of children and families. This will involve, as Jordanova suggested, self-awareness and sensitivity. But it should not be sidetracked by ideological debates. As Cunningham observed, the stakes for modern children and families are high. To make children’s history useful for the present, historians of children and families need to put aside their differences and get back to work.

Some comments on the structure:   As in the previous example, the writer includes the perspectives of other commentators. This is especially common in essays on secondary sources in history, because “historiography” is often imagined as an ongoing conversation about primary and important secondary texts. The “so what” statement is more explicit this time, relating the study of children in the past to improving the lives of children and families today. The importance of connecting with the needs of today is problematic (many historians would criticize this as “presentism”); so the writer includes a supporting perspective from a sympathetic commentator.

From an essay in which the writer compares and contrasts the character she is examining with a character from another work:

Like Ophelia, Gretchen has moments of confusion and despair, but she decides to give in to her feelings and take responsibility for them. By having Gretchen freely stay behind to face her execution, Goethe casts aside any similarities that his character shares with Shakespeare’s Ophelia. Along with the empowering freedom of Gretchen’s striving comes the struggle to act rightly. But if no objective absolutes exist, according to Goethe’s God, on what basis can Gretchen make her decisions in order to be saved? She comes to the realization that the only absolutes exist within herself. Goethe’s God saves her, not for being a penitent Christian, but for staying true to these self-imposed absolutes.

Some comments on the structure:  Another strong conclusion. The writer’s interpretation could be contested, but she has argued it well and convincingly throughout the essay and concluded strongly. Incidentally, note also that by specifying “Goethe’s God” in her interpretation she avoids any distracting discussion of religion and keeps her writing focused on literary analysis. We don’t argue the nature of “God” in an essay about literature; only the nature of the “God” in the text.

A Short Handbook for writing essays in the Humanities and Social Sciences Copyright © 2019 by Salvatore F. Allosso and Dan Allosso is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• Kindergarten
• Middle School
• High School
• Math Worksheets
• Language Arts
• Social Studies

Coherence Examples

Writing that has coherence is writing that has logical connections between ideas. The word coherence literally means "to stick together" in Latin.

Coherent writing is produced when the writer has a strong organizational structure. There are connections and transitions between ideas in sentences, paragraphs, and the entire text. Coherence is also more easily achieved when the writer uses strong sequencing of ideas and events in the writing. Coherence can refer to sentences and small portions of the text-such as a paragraph or chapter. It can also refer to the text as a whole-is the entire argument coherent .

Examples of Coherence from Literature and Essays

Notice the logical sequencing of the argument in this paragraph on the need for school uniforms. Coherence is produced through the use of transition words and phrases that lead from the topic sentence to specific examples and explanation.

School uniforms are necessary due to the divisions and cliques that are often created when uniforms are not present. In many urban areas, clothing is often used to signify membership in or allegiance to a specific street gang. Gang activity and violence distracts from a safe and effective school environment, and through the discipline process, students are excluded from the classroom. In addition, outside of street gang activity, clothing often creates cliques in schools. In a recent interview, students in one middle school discussed a specific clique of girls based on their attire, including "scrunchies" and "large sweatshirts that cover their shorts." Solely based on attire, students recognized members of this clique in their schools, and saw themselves as excluded from the group. School uniforms would create an environment where all students dress similarly and no student is singled out as a member of a specific clique.

In this speech from Julius Caesar , Shakespeare creates coherence through the use of examples and repetition as Marc Antony questions Brutus' character.

Friends, Romans, countrymen, lend me your ears; I come to bury Caesar, not to praise him. The evil that men do lives after them; The good is oft interred with their bones; So let it be with Caesar. The noble Brutus Hath told you Caesar was ambitious: If it were so, it was a grievous fault, And grievously hath Caesar answer'd it. Here, under leave of Brutus and the rest- For Brutus is an honourable man; So are they all, all honourable men- Come I to speak in Caesar's funeral. He was my friend, faithful and just to me: But Brutus says he was ambitious; And Brutus is an honourable man. He hath brought many captives home to Rome Whose ransoms did the general coffers fill: Did this in Caesar seem ambitious? When that the poor have cried, Caesar hath wept: Ambition should be made of sterner stuff: Yet Brutus says he was ambitious; And Brutus is an honourable man. You all did see that on the Lupercal I thrice presented him a kingly crown, Which he did thrice refuse: was this ambition? Yet Brutus says he was ambitious; And, sure, he is an honourable man. I speak not to disprove what Brutus spoke, But here I am to speak what I do know. You all did love him once, not without cause: What cause withholds you then, to mourn for him? O judgment! thou art fled to brutish beasts, And men have lost their reason. Bear with me; My heart is in the coffin there with Caesar, And I must pause till it come back to me.

More Topics

• Handwriting
• Difference Between
• 2020 Calendar
• Online Calculators
• Multiplication

Educational Videos

• Coloring Pages
• Privacy policy
• Terms of Use

© 2005-2020 Softschools.com

• Newsletters
• Account Activating this button will toggle the display of additional content Account Sign out

The Donald Trump I Saw on The Apprentice

For 20 years, i couldn’t say what i watched the former president do on the set of the show that changed everything. now i can..

On Jan. 8, 2004, just more than 20 years ago, the first episode of The Apprentice aired. It was called “Meet the Billionaire,” and 18 million people watched. The episodes that followed climbed to roughly 20 million each week. A staggering 28 million viewers tuned in to watch the first season finale. The series won an Emmy nomination for Outstanding Reality-Competition Program, and the Television Critics Association called it one of the best TV shows of the year, alongside The Sopranos and Arrested Development . The series—alongside its bawdy sibling, The Celebrity Apprentice —appeared on NBC in coveted prime-time slots for more than a decade.

The Apprentice was an instant success in another way too. It elevated Donald J. Trump from sleazy New York tabloid hustler to respectable household name. In the show, he appeared to demonstrate impeccable business instincts and unparalleled wealth, even though his businesses had barely survived multiple bankruptcies and faced yet another when he was cast. By carefully misleading viewers about Trump—his wealth, his stature, his character, and his intent—the competition reality show set about an American fraud that would balloon beyond its creators’ wildest imaginations.

I should know. I was one of four producers involved in the first two seasons. During that time, I signed an expansive nondisclosure agreement that promised a fine of $5 million and even jail time if I were to ever divulge what actually happened. It expired this year.

No one involved in The Apprentice —from the production company or the network, to the cast and crew—was involved in a con with malicious intent. It was a TV show , and it was made for entertainment . I still believe that. But we played fast and loose with the facts, particularly regarding Trump, and if you were one of the 28 million who tuned in, chances are you were conned.

As Trump answers for another of his alleged deception schemes in New York and gears up to try to persuade Americans to elect him again, in part thanks to the myth we created, I can finally tell you what making Trump into what he is today looked like from my side. Most days were revealing. Some still haunt me, two decades later.

Nearly everything I ever learned about deception I learned from my friend Apollo Robbins. He’s been called a professional pickpocket, but he’s actually a “perceptions expert.” Apollo has spent his life studying the psychology of how we distort other people’s perceptions of reality and has done so by picking pockets onstage for the entertainment of others. He is a master of deception, a skill that made him, back in the day, the so-called best-kept secret in Las Vegas. After “fanning” his marks with casual, unobtrusive touch designed to make them feel safe or at ease, Apollo determines where the items reside—the wallet inside a breast pocket, the Rolex fastened to a wrist—and he removes these items without detection. He’ll even tell you what he intends to steal before he does it. He does this not to hurt people or bewilder them with a puzzle but to challenge their maps of reality. The results are marvelous. A lot of magic is designed to appeal to people visually, but what he’s trying to affect is your mind, your moods, your perceptions.

As a producer working in unscripted, or “reality,” television, I have the same goal. Like Apollo, I want to entertain, make people joyful, maybe even challenge their ways of thinking. But because I often lack the cinematic power of a movie, with its visual pyrotechnics or rehearsed dialogue, I rely on shaping the perceptions of viewers, manipulating their maps of reality toward something I want them to think or feel.

The presumption is that reality TV is scripted. What actually happens is the illusion of reality by staging situations against an authentic backdrop. The more authentic it is to, say, have a 40-foot wave bearing down on a crab boat in the Bering Sea for Deadliest Catch , the more we can trick you into thinking a malevolent Russian trawler is out there messing with the crabber’s bait. There is a trick to it, and when it works, you feel as if you’re watching a scripted show. Although very few programs are out-and-out fake, there is deception at play in every single reality program. The producers and editors are ostensibly con artists, distracting you with grand notions while we steal from you your precious time.

But the real con that drove The Apprentice is far older than television. The “pig in the poke” comes from an idiom dating to 1555: “I’ll never buy a pig in a poke / There’s many a foul pig in a fair cloak.” It refers to the time-honored scam of selling a suckling pig at market but handing over a bag (the poke) to the purchaser, who never looks inside it. Eventually, he discovers he’s purchased something quite different.

Our show became a 21 st -century version. It’s a long con played out over a decade of watching Trump dominate prime time by shouting orders, appearing to lead, and confidently firing some of the most capable people on television, all before awarding one eligible person a job. Audiences responded to Trump’s arrogance, his perceived abilities and prescience, but mostly his confidence . The centerpiece to any confidence game is precisely that— confidence .

As I walk into my interview for The Apprentice , I inadvertently learn how important it is for every one of us involved to demonstrate confidence above all else.

I sit down with Jay Bienstock, the showrunner, who has one last producer position to fill and needs somebody capable and hardworking. His office is sparse, and the desk is strategically placed directly across from the couch, with a noticeable angle downward from his desk to whomever is seated across from him. (I’m recalling all of the quoted conversations here to the best of my ability; they are not verbatim.)

He is smiling and even laughing throughout the interview, but from the steep angle at which he gazes down on me, there is no mistaking who is in charge. He seems to like what he hears and offers to follow up with my agent. “But I have to check your references before I can hire you,” he says. “You’d be crazy not to,” I reply. He laughs, claps his hands together, and grins. “ THAT’S what I’m talking about,” he says. “That’s the confidence this show needs!”

I sit there, several inches below eyeline, and ponder what just happened. What, I wonder, is so “confident” about suggesting he’d be crazy to not check my references? Then it dawns on me. He thinks I meant “You’d be crazy not to hire me.” The signal to noise begins.

Listen to Bill Pruitt discuss this story on What Next , Slate’s daily news podcast:

Before I leave, I have to ask: Why Trump? Bienstock discovers that we both lived in New York for a time. Knowing what we know about Trump, selling the idea that intelligent people would compete to land a job working for him will be a challenge.

“The idea is to have a new and different billionaire every season—just like there’s a new and different island on Survivor . We reached out to Spielberg, Katzenberg, Geffen, among others,” he says. “Trump is the only one who agreed to sign on.” (Bienstock didn’t respond to a request for comment.)

“We’ll make it work,” Bienstock says confidently. I rise, shake his hand, and leave, and head over to Dutton’s bookstore to pick up a used copy of Trump’s The Art of the Deal . It is filled with takeaways about branding and strategizing but conveniently omits Trump bluffing his way through meetings with contractors, stiffing them when it is convenient to do so, and betraying his most trusted colleagues to get what he wants. (The book’s ghostwriter, Tony Schwartz, has since tried to get the bestseller recategorized in the Library of Congress as a work of fiction.)

Another show of confidence is the budget the series commands. It’s not as expensive as a scripted series, but for a reality show, the price is high. Never have I worked on a series with this level of funding, but the cost is justified. This needs to feel real.

New York City is the perfect—though expensive—backdrop. Trump’s actual offices are, however, less than telegenic. They are cramped, and a lot of the wood furniture is chipped or peeling. None of it is suitable to appear on camera. We need what grifters call the Big Store: a fake but authentic-looking establishment in which the con goes down. Trump Tower, at the time, is mostly condos and some offices situated in the high-rise. The mezzanine comprises vacant and overpriced retail space, all of it unfinished. Trump offers the space to the production—at a premium, naturally—and it is inside this location that we create our own “reception area” with doors leading to a fake, dimly lit, and appropriately ominous-feeling “boardroom.”

Next door, there’s the “suite” where the contestants will live, which is made to look like a trendy loft-style apartment they all share. The lodgings are made up of partitions surrounding tiny, hard bunks upon which the candidates sleep; the illusion comes from elegantly appointed common areas, where most of the interplay will go down.

During a tour of the set, I have my first encounter with Trump. I leave the suite and enter the gear room, the only vacant retail space that will remain unfinished. It is filled with equipment and crew members milling about. In walks a trio of men. In the middle is Trump, in a navy blue suit and scarlet tie. He’s surprisingly tall, and not just because of the hair. He is flanked by two even taller men. Bienstock makes introductions, and I watch as Trump shakes hands with everyone. I’d been told he would never do this, something about fearing unwanted germs. When it is my turn, I decide on the convivial two-hander and place my right hand into his and my left onto his wrist as we shake. His eye contact is limited but thorough. He is sizing me up. He looks like a wolf about to rip my throat out before turning away, offering me my first glimpse at the superstructure—his hairstyle—buttressed atop his head with what must be gallons of Aqua Net.

I watch as Trump saunters around the room, snatches up a fistful of M&Ms from the craft service table set aside for the crew, and shoves them into his mouth. Then he is gone, ushered away toward some important meeting he must attend, as if to say, to one and all present, This is unimportant .

Eventually, it’s time to roll cameras. When Trump is called to perform, we are filming the first scene of the first episode on the floor of the New York Stock Exchange, and he is about to deliver the first task. Filming inside this beacon of capitalism and wealth gives the series the legitimacy it needs. A con artist would call staging the scam inside a legitimate institution “playing a man against the wall.”

From the balcony overlooking the famed trading room floor, Trump will set up the entire premise of the show on camera and engage in a little banter with the other participants. This includes introducing his advisers, George Ross, an older, grouchy attorney devoted to Trump’s legal affairs, and Carolyn Kepcher, a perpetual skeptic who runs his hospitality units and one of his golf clubs. (They might be called “the shills,” others in on the con who will act as Trump’s eyes and ears.)

The contestants are there, lined up and zeroed in on by camera operators getting reaction shots to whatever it is Trump says. Although they mostly just stand and wait, they patiently go along with the proceedings. They are not in on the con. They act as “the little blind mice,” who, in fraudster terms, convey a sense of authenticity by reacting to the goings-on, like lab rats caught in a maze.

Nothing is scripted—except for what Trump needs to say. Cue cards are present, but mostly it is Bienstock running up, coaching Trump, tossing out suggestions from the script he has written for the man. The feeling is that while doing a fair job of repeating the necessary words verbatim, Trump also appears to be inadvertently shouting at the contestants. His hands shuttle back and forth as if holding an invisible accordion, a gesture now famous in memes .

Each episode is filmed over three days. For the first episode, the two teams of contestants, divided by gender, take to the streets to carry out the initial task of trying to sell lemonade for the most money. The women pulverize the men.

Having won, the women are invited upstairs for a direct look at Trump’s very own apartment in Trump Tower, a reward designed specifically to introduce viewers to the gaudy but elevated world of Donald Trump at home. The men, who lost, go back to the loft to await their fate at the hands of Trump. He will be sending one of them home.

Inside the now-empty boardroom set, a meeting with the producers is called for the first briefing of Trump before the anticipated firing. With Trump are his cronies, Ross and Kepcher. Trump is “too busy,” so they have each observed both teams in the field and make an assessment of who prevailed and who fell behind.

Now, this is important. The Apprentice is a game show regulated by the Federal Communications Commission. In the 1950s, scandals arose when producers of quiz shows fed answers to likable, ratings-generating contestants while withholding those answers from unlikable but truly knowledgeable players. Any of us involved in The Apprentice swinging the outcome of prize money by telling Trump whom to fire is forbidden.

Considering this, Bienstock wisely chooses to record these off-camera briefings in case the FCC ever rolls up on us. Rather than blurt out who they think should get canned, the two producers of that week’s episode—each following one team—are coached to equitably share with Trump the virtues and deficiencies of each member of the losing team. This renders a balanced depiction of how and why they lost. There are obvious choices of whom to fire, but we want it to be something of a horse race, to sustain the drama and keep people watching.

Satisfied he has what he needs, Trump dismisses the prefiring discussion with the wave of a hand, claiming he has places to be, let’s get on with it, etc. We proceed to set up for what will be our first boardroom.

The producers retreat to the adjacent control room to watch the event unfold. Per the show’s format, the losing team is summoned in anticipation of one of its members being sent home. Leaving their luggage in the reception area, the men walk into the boardroom, where Trump is flanked by Ross and Kepcher, waiting for them solemnly. Trump just frowns from a gigantic red leather chair, his eyeline noticeably well above those sitting across from him.

The men proceed to verbally go after one another like gladiators jousting before the emperor. Trump takes the conversation into potentially dangerous terrain, asking one contestant, who is Jewish, whether he believes in “the genetic pool.” The contestant’s retort is swift and resolute: He tells Trump that he does, in fact, have the genes, “just like you got from your father, Fred Trump, and your mother, Mary Trump.” It pours out of him. It is dramatic. It is good reality TV.

The project manager must then choose two of the men to come back to the boardroom with him while everyone else is dismissed. An off-camera prefiring consultation with Trump takes place (and is recorded), right before the three men are brought back for the eventual firing. We film Trump, Ross, and Kepcher deliberating and giving the pluses and minuses of each, remarking on how risky it was for one of the contestants to stand up for himself the way that he did. Trump turns back and forth to each, listening. His cronies stick to their stories and give added deferential treatment toward Trump, with Ross strategically reminding him, “You’ve been taking risks your entire life.”

Trump summons the three men back into the boardroom for final judging. Trump grills one and says, “I will let you stay.” ( Wow! we think. A benevolent leader. ) When he turns his attention to the other man—the one he asked about genetics—it looks clear. He is doomed. So much so that the man stands when Trump tells him, “It seems unanimous.” Trump then offhandedly tells him to sit down, calling him “a wild card,” echoing Ross’ earlier observation of the boss, Trump.

After this comes an unwieldy moment when, at the behest of Bienstock, Trump fumbles through a given line. “We have an elevator,” he says to the remaining contestant, named David, “that goes up to the suite and an elevator that goes down”—he pauses to recall the exact wording—“to the street. And, David, I’m going to ask you to take the down elevator.”

The men react and awkwardly rise. It is an unsatisfactory conclusion, given all the preceding drama.

From the control room, we all watch as the three men depart the boardroom. A quick huddle takes place between the producers and the executive from NBC. We bolt from the control room out into the boardroom and confer with Trump, telling him we will need him to say something more direct to conclude the moment when David is let go.

“Well, I’d probably just fire him,” Trump says. “Why not just say that?” Bienstock asks. “Fine,” Trump says.

We return to the control room. The three men from the losing team are brought back into the boardroom, and Trump repeats his line about the elevator, then turns to David, who already knows his fate, and adds, “David, you’re fired.”

The line insertion happened in a perilously scripted way, but it is deemed satisfactory. “You’re fired” becomes the expression we will stick with. It works. Trump comes off as decisive and to the point.

Later, Trump will try to trademark “You’re fired.” He is not successful.

Trump’s appearances make up so little of our shooting schedule that whenever he shows up to film, it isn’t just the wild-card on-camera moments we both hope for and are terrified of that put everyone on edge. It is the way he, the star (and half owner) of the show, targets people on the crew with the gaze of a hungry lion.

While leering at a female camera assistant or assessing the physical attributes of a female contestant for whoever is listening, he orders a female camera operator off an elevator on which she is about to film him. “She’s too heavy,” I hear him say.

Another female camera operator, who happens to have blond hair and blue eyes, draws from Trump comparisons to his own Ivanka Trump. “There’s a beautiful woman behind that camera,” he says toward a line of 10 different operators set up in the foyer of Trump Tower one day. “That’s all I want to look at.”

Trump corners a female producer and asks her whom he should fire. She demurs, saying something about how one of the contestants blamed another for their team losing. Trump then raises his hands, cupping them to his chest: “You mean the one with the …?” He doesn’t know the contestant’s name. Trump eventually fires her.

(In response to detailed questions about this and other incidents reported in this article, Steven Cheung, a spokesman for the Trump 2024 campaign, wrote, “This is a completely fabricated and bullshit story that was already peddled in 2016.” He said that it is surfacing now because Democrats are “desperate.”)

Trump goes about knocking off every one of the contestants in the boardroom until only two remain. The finalists are Kwame Jackson, a Black broker from Goldman Sachs, and Bill Rancic, a white entrepreneur from Chicago who runs his own cigar business. Trump assigns them each a task devoted to one of his crown-jewel properties. Jackson will oversee a Jessica Simpson benefit concert at Trump Taj Mahal Casino in Atlantic City, while Rancic will oversee a celebrity golf tournament at Trump National Golf Club in Briarcliff Manor, New York.

Viewers need to believe that whatever Trump touches turns to gold. These properties that bear his name are supposed to glitter and gleam. All thanks to him.

Reality is another matter altogether. The lights in the casino’s sign are out. Hong Kong investors actually own the place—Trump merely lends his name. The carpet stinks, and the surroundings for Simpson’s concert are ramshackle at best. We shoot around all that.

Both Rancic and Jackson do a round-robin recruitment of former contestants, and Jackson makes the fateful decision to team up with the notorious Omarosa, among others, to help him carry out his final challenge.

With her tenure on the series nearly over, Omarosa launches several simultaneous attacks on her fellow teammates in support of her “brother” Kwame. For the fame-seeking beauty queen, it is a do-or-die play for some much-coveted screen time. As on previous tasks, Ross and Kepcher will observe both events.

Over at Trump National Golf Club, where I am stationed, it is sunny and bright, set against luscious fall colors. I am driven up to the golf club from Manhattan to scout. With me are the other producers, all of whom are men. We meet Trump at one of the homes he keeps for himself on the grounds of the club.

“Melania doesn’t even know about this place,” he says out loud to us, snickering, implying that the home’s function is as his personal lair for his sexual exploits, all of which are unknown to his then-fiancée Melania Knauss.

We are taken around the rest of the club’s property and told what to feature on camera and what to stay away from. The clubhouse is a particularly necessary inclusion, and it is inside these luxurious confines where I have the privilege of meeting the architect. Finding myself alone with him, I make a point of commending him for what I feel is a remarkable building. The place is genuinely spectacular. He thanks me.

“It’s bittersweet,” he tells me. “I’m very proud of this place, but …” He hesitates. “I wasn’t paid what was promised,” he says. I just listen. “Trump pays half upfront,” he says, “but he’ll stiff you for the rest once the project is completed.”

“He stiffed you?”

“If I tried to sue, the legal bills would be more than what I was owed. He knew that. He basically said Take what I’m offering ,” and I see how heavy this is for the man, all these years later. “So, we sent the invoice. He didn’t even pay that,” he says. None of this will be in the show. Not Trump’s suggested infidelities, nor his aversion toward paying those who work for him.

When the tasks are over, we are back in the boardroom, having our conference with Trump about how the two finalists compare—a conversation that I know to be recorded. We huddle around him and set up the last moments of the candidates, Jackson and Rancic.

Trump will make his decision live on camera months later, so what we are about to film is the setup to that reveal. The race between Jackson and Rancic should seem close, and that’s how we’ll edit the footage. Since we don’t know who’ll be chosen, it must appear close, even if it’s not.

We lay out the virtues and deficiencies of each finalist to Trump in a fair and balanced way, but sensing the moment at hand, Kepcher sort of comes out of herself. She expresses how she observed Jackson at the casino overcoming more obstacles than Rancic, particularly with the way he managed the troublesome Omarosa. Jackson, Kepcher maintains, handled the calamity with grace.

“I think Kwame would be a great addition to the organization,” Kepcher says to Trump, who winces while his head bobs around in reaction to what he is hearing and clearly resisting.

“Why didn’t he just fire her?” Trump asks, referring to Omarosa. It’s a reasonable question. Given that this the first time we’ve ever been in this situation, none of this is something we expected.

“That’s not his job,” Bienstock says to Trump. “That’s yours.” Trump’s head continues to bob.

“I don’t think he knew he had the ability to do that,” Kepcher says. Trump winces again.

“Yeah,” he says to no one in particular, “but, I mean, would America buy a n— winning?”

Kepcher’s pale skin goes bright red. I turn my gaze toward Trump. He continues to wince. He is serious, and he is adamant about not hiring Jackson.

Bienstock does a half cough, half laugh, and swiftly changes the topic or throws to Ross for his assessment. What happens next I don’t entirely recall. I am still processing what I have just heard. We all are. Only Bienstock knows well enough to keep the train moving. None of us thinks to walk out the door and never return. I still wish I had. (Bienstock and Kepcher didn’t respond to requests for comment.)

Afterward, we film the final meeting in the boardroom, where Jackson and Rancic are scrutinized by Trump, who, we already know, favors Rancic. Then we wrap production, pack up, and head home. There is no discussion about what Trump said in the boardroom, about how the damning evidence was caught on tape. Nothing happens.

We go home and face the next phase of our assignment, the editing. In stitching the footage together, the swindle we are now involved in ascends to new levels.

Editing in a reality TV show is what script writing is to a narrative series. A lot of effort goes into the storytelling because, basically, in every single unscripted series—whether it’s a daytime talk show, an adventure documentary, or a shiny floor dance-off—there are three versions: There’s what happens, there’s what gets filmed, and there’s what gets cut down into 43 minutes squeezed between commercial breaks. Especially for a competition series, it’s important that the third version represent the first as much as possible. A defeated contestant could show up in the press and cry foul if they’re misrepresented. Best to let people fail of their own accord. That said, we look after our prized possessions in how we edit the series, and some people fare better than others.

We attend to our thesis that only the best and brightest deserve a job working for Donald Trump. Luckily, the winner, Bill Rancic, and his rival, Kwame Jackson, come off as capable and confident throughout the season. If for some reason they had not, we would have conveniently left their shortcomings on the cutting room floor. In actuality, both men did deserve to win.

Without a doubt, the hardest decisions we faced in postproduction were how to edit together sequences involving Trump. We needed him to sound sharp, dignified, and clear on what he was looking for and not as if he was yelling at people. You see him today: When he reads from a teleprompter, he comes off as loud and stoic. Go to one of his rallies and he’s the off-the-cuff rambler rousing his followers into a frenzy. While filming, he struggled to convey even the most basic items. But as he became more comfortable with filming, Trump made raucous comments he found funny or amusing—some of them misogynistic as well as racist. We cut those comments. Go to one of his rallies today and you can hear many of them.

If you listen carefully, especially to that first episode, you will notice clearly altered dialogue from Trump in both the task delivery and the boardroom. Trump was overwhelmed with remembering the contestants’ names, the way they would ride the elevator back upstairs or down to the street, the mechanics of what he needed to convey. Bienstock instigated additional dialogue recording that came late in the edit phase. We set Trump up in the soundproof boardroom set and fed him lines he would read into a microphone with Bienstock on the phone, directing from L.A. And suddenly Trump knows the names of every one of the contestants and says them while the camera cuts to each of their faces. Wow , you think, how does he remember everyone’s name? While on location, he could barely put a sentence together regarding how a task would work. Listen now, and he speaks directly to what needs to happen while the camera conveniently cuts away to the contestants, who are listening and nodding. He sounds articulate and concise through some editing sleight of hand.

Then comes the note from NBC about the fact that after Trump delivers the task assignment to the contestants, he disappears from the episode after the first act and doesn’t show up again until the next-to-last. That’s too long for the (high-priced) star of the show to be absent.

There is a convenient solution. At the top of the second act, right after the task has been assigned but right before the teams embark on their assignment, we insert a sequence with Trump, seated inside his gilded apartment, dispensing a carefully crafted bit of wisdom. He speaks to whatever the theme of each episode is—why someone gets fired or what would lead to a win. The net effect is not only that Trump appears once more in each episode but that he also now seems prophetic in how he just knows the way things will go right or wrong with each individual task. He comes off as all-seeing and all-knowing. We are led to believe that Donald Trump is a natural-born leader.

Through the editorial nudge we provide him, Trump prevails. So much so that NBC asks for more time in the boardroom to appear at the end of all the remaining episodes. (NBC declined to comment for this article.)

When it comes to the long con, the cherry on top is the prologue to the premiere. It’s a five-minute-long soliloquy delivered by Trump at the beginning of the first episode, the one titled “Meet the Billionaire.” Over a rousing score, it features Trump pulling out all the stops, calling New York “ my city” and confessing to crawling out from under “billions of dollars in debt.” There’s Trump in the back of limousines. Trump arriving before throngs of cheering crowds outside Trump Tower. Trump in his very own helicopter as it banks over midtown—the same helicopter with the Trump logo that, just like the airplane, is actually for sale to the highest bidder. The truth is, almost nothing was how we made it seem.

So, we scammed. We swindled. Nobody heard the racist and misogynistic comments or saw the alleged cheating, the bluffing, or his hair taking off in the wind. Those tapes, I’ve come to believe, will never be found.

No one lost their retirement fund or fell on hard times from watching The Apprentice . But Trump rose in stature to the point where he could finally eye a run for the White House, something he had intended to do all the way back in 1998. Along the way, he could now feed his appetite for defrauding the public with various shady practices.

In 2005 thousands of students enrolled in what was called Trump University, hoping to gain insight from the Donald and his “handpicked” professors. Each paid as much as $35,000 to listen to some huckster trade on Trump’s name. In a sworn affidavit, salesman Ronald Schnackenberg testified that Trump University was “fraudulent.” The scam swiftly went from online videoconferencing courses to live events held by high-pressure sales professionals whose only job was to persuade attendees to sign up for the course. The sales were for the course “tuition” and had nothing whatsoever to do with real estate investments. A class action suit was filed against Trump.

That same year, Trump was caught bragging to Access Hollywood co-host Billy Bush that he likes to grab married women “by the pussy,” adding, “When you’re a star, they let you do it.” He later tried to recruit porn actor Stormy Daniels for The Apprentice despite her profession and, according to Daniels, had sex with her right after his last son was born. (His alleged attempt to pay off Daniels is, of course, the subject of his recent trial.)

In October 2016—a month before the election—the Access Hollywood tapes were released and written off as “locker room banter.” Trump paid Daniels to keep silent about their alleged affair. He paid $25 million to settle the Trump University lawsuit and make it go away.

He went on to become the first elected president to possess neither public service nor military experience. And although he lost the popular vote, Trump beat out Hillary Clinton in the Electoral College, winning in the Rust Belt by just 80,000 votes.

Trump has been called the “reality TV president,” and not just because of The Apprentice . The Situation Room, where top advisers gathered, became a place for photo-ops, a bigger, better boardroom. Trump swaggered and cajoled, just as he had on the show. Whom would he listen to? Whom would he fire? Stay tuned. Trump even has his own spinoff, called the House of Representatives, where women hurl racist taunts and body-shame one another with impunity. The State of the Union is basically a cage fight. The demands of public office now include blowhard buffoonery.

I reached out to Apollo, the Vegas perceptions expert, to discuss all of this. He reminded me how if a person wants to manipulate the signal, they simply turn up the noise. “In a world that is so uncertain,” he said, “a confidence man comes along and fills in the blanks. The more confident they are, the more we’re inclined to go along with what they suggest.”

A reality TV show gave rise to an avaricious hustler, and a deal was made: Subvert the facts, look past the deficiencies, deceive where necessary, and prevail in the name of television ratings and good, clean fun.

Trump is making another run at the White House and is leading in certain polls. People I know enthusiastically support him and expect he’ll return to office. It’s not just hats, sneakers, a fragrance, or Bibles. Donald Trump is selling his vision of the world, and people are buying it.

Knowing all they know, how could these people still think he’s capable of being president of the United States?

Perhaps they watched our show and were conned by the pig in the poke.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 03 June 2024

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

• Wenchao Li 1 &
• Haitao Liu 2  

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

185 Accesses

2 Altmetric

Metrics details

• Language and linguistics

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.

Similar content being viewed by others

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Testing theory of mind in large language models and humans

Highly accurate protein structure prediction with AlphaFold

Conventional machine learning technology in aes.

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

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

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

Deep learning technology in AES

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

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

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

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

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

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

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

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

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

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

Inflectional

give, give-s, gave, given, giving

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

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

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

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

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

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

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

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

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

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

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

The research questions addressed in this study are as follows:

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

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

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

Methodology

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

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

Measures of writing proficiency for nonnative Japanese

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

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Criteria (output indicator)

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

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

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

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

Moving average type-token ratio.

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

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

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

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

Number of errors per essay.

Japanese essay text

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

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

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

Statistical analysis

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

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

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

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

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

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

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

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

Step 1: Construct a weight matrix W as follows:

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

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

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

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

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

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

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

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

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

Results and discussion

Annotation reliability of the llm.

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

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

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

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

Inter-annotator agreement (human–human annotator agreement)

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

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

Agreement of annotation outcomes between human and LLM

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

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

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

Reliability of LLM-driven AES scoring

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

Reliability of LLM-driven AES discriminating proficiency levels

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

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

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

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

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

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

Prompt strategy

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

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

PRMSE scores of the 18 AES models.

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

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

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

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

Data availability

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

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

SPOT is a specific component of the TTBJ test.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Article   Google Scholar  

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

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

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

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

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

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

Google Scholar  

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

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

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

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

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

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

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

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

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

Article   CAS   PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

Article   PubMed   Google Scholar  

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

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

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

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

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

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

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

Article   MathSciNet   Google Scholar  

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

PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Download references

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

Author information

Authors and affiliations.

Department of Japanese Studies, Zhejiang University, Hangzhou, China

Department of Linguistics and Applied Linguistics, Zhejiang University, Hangzhou, China

You can also search for this author in PubMed   Google Scholar

Contributions

Wenchao Li is in charge of conceptualization, validation, formal analysis, investigation, data curation, visualization and writing the draft. Haitao Liu is in charge of supervision.

Corresponding author

Correspondence to Wenchao Li .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

Ethical approval was not required as the study did not involve human participants.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplemental material file #1, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 02 February 2024

Accepted : 16 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1057/s41599-024-03209-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

British Council Malaysia

• English schools
• myClass Login
• Show search Search Search Close search

How to write a cohesive essay

When it comes to writing, people usually emphasise the importance of good grammar and proper spelling. However, there is a third element that actually helps authors get their thoughts across to readers, that is cohesiveness in writing. 

In writing, cohesiveness is the quality that makes it easier for people to read and understand an essay’s content. A cohesive essay has all its parts (beginning, middle, and end) united, supporting each other to inform or convince the reader. 

Unfortunately, this is an element that even intermediate or advanced writers stumble on. While the writer’s thoughts are in their compositions, all too often readers find it difficult to understand what is being said because of the poor organisation of ideas. This article provides tips on how you can make your essay cohesive. 

1. Identify the thesis statement of your essay

A thesis statement states what your position is regarding the topic you are discussing. To make an essay worth reading, you will need to make sure that you have a compelling stance.

However, identifying the thesis statement is only the first step. Each element that you put in your essay should be included in a way that supports your argument, which should be the focus of your writing. If you feel that some of the thoughts you initially included do not contribute to strengthening your position, it might be better to take them out when you revise your essay to have a more powerful piece. 

2. Create an outline 

One of the common mistakes made by writers is that they tend to add a lot of details to their essay which, while interesting, may not really be relevant to the topic at hand. Another problem is jumping from one thought to another, which can confuse a reader if they are not familiar with the subject.

Preparing an outline can help you avoid these difficulties. List the ideas you have in mind for your essay, and then see if you can arrange these thoughts in a way that would make it easy for your readers to understand what you are saying. 

While discursive essays do not usually contain stories, the same principle still applies. Your writing should have an introduction, a discussion portion and a conclusion. Again, make sure that each segment supports and strengthens your thesis statement.

As a side note, a good way to write the conclusion of your essay is to mention the points that you raised in your introduction. At the same time, you should use this section to summarise main ideas and restate your position to drive the message home to your readers. 

3. Make sure everything is connected

In connection to the previous point, make sure that each section of your essay is linked to the one after it. Think of your essay as a story: it should have a beginning, middle, and end, and the way that you write your piece should logically tie these elements together in a linear manner. 

4. Proofread before submitting your essay

Make sure to review your composition prior to submission. In most cases, the first draft may be a bit disorganised because this is the first time that your thoughts have been laid out on paper. By reviewing what you have written, you will be able to see which parts need editing, and which ones can be rearranged to make your essay more easily understood by your readers. Try to look at what you wrote from the point of view of your audience. Will they be able to understand your train of thought, or do you need to reorganise some parts to make it easier for them to appreciate what you are saying? Taking another look at your essay and editing it can do wonders for how your composition flows.

Writing a cohesive essay could be a lot easier than you think – especially when you follow these steps. Don’t forget that reading complements writing: try reading essays on various topics and see if each of their parts supports their identified goal or argument.

Advertisement

What to Make of the Jobs Report’s Mixed Signals

The monthly data is drawn from two surveys. This time, one was quite strong, and the other flashed warning signs.

• Share full article

By Ben Casselman

• June 7, 2024, 10:07 a.m. ET

Sometimes, the many numbers included in the government’s monthly jobs report come together to paint a clear, coherent picture of the strength or weakness of the U.S. labor market.

This is not one of those times.

Instead, the data released by the Labor Department on Friday was a mess of conflicting signals. It couldn’t even agree on the most basic of questions: whether the economy is adding or losing jobs.

The report showed that employers added 272,000 nonagricultural jobs in May, far more than forecasters were expecting. That figure is based on a survey of about 119,000 businesses, nonprofit organizations and government agencies.

But the report also contains data from another survey, of about 60,000 households. That data showed that the number of people who were employed last month actually fell by 408,000, while the unemployment rate rose to 4 percent for the first time in more than two years.

The two surveys measure slightly different things. The employer survey includes only employees, for example, while the household survey includes independent contractors and self-employed workers. But that doesn’t explain the discrepancy last month: Adjusting the household survey to align with the concepts used in the employer survey makes the job losses in May look larger, not smaller.

That means that the conflicting pictures come down to some combination of measurement error and random noise. That is frustrating but not unusual: Over the long term, the two surveys generally tell similar stories, but over shorter periods they frequently diverge.

Economists typically put more weight on the employer survey, which is much larger and is generally viewed as more reliable. But they aren’t sure which data to believe this time around. Some economists have argued that the household survey could be failing to capture fully the recent wave of immigration, leading it to undercount employment growth. But others have argued that the employer survey could be overstating hiring because it isn’t accounting properly for recent business failures, among other factors.

Ben Casselman writes about economics with a particular focus on stories involving data. He has covered the economy for nearly 20 years, and his recent work has focused on how trends in labor, politics, technology and demographics have shaped the way we live and work. More about Ben Casselman