essay writing tense

Verb Tenses

What this handout is about.

The present simple, past simple, and present perfect verb tenses account for approximately 80% of verb tense use in academic writing. This handout will help you understand how to use these three verb tenses in your own academic writing.

Click here for a color-coded illustration of changing verb tenses in academic writing.

Present simple tense

The present simple tense is used:

In your introduction, the present simple tense describes what we already know about the topic. In the conclusion, it says what we now know about the topic and what further research is still needed.

“The data suggest…” “The research shows…”

“The dinoflagellate’s TFVCs require an unidentified substance in fresh fish excreta” (Penrose and Katz, 330).

“There is evidence that…”

“So I’m walking through the park yesterday, and I hear all of this loud music and yelling. Turns out, there’s a free concert!” “Shakespeare captures human nature so accurately.”

Past simple tense

Past simple tense is used for two main functions in most academic fields.

“…customers obviously want to be treated at least as well on fishing vessels as they are by other recreation businesses. [General claim using simple present] De Young (1987) found the quality of service to be more important than catching fish in attracting repeat customers. [Specific claim from a previous study using simple past] (Marine Science)

We conducted a secondary data analysis… (Public Health) Descriptional statistical tests and t-student test were used for statistical analysis. (Medicine) The control group of students took the course previously… (Education)

Present perfect tense

The present perfect acts as a “bridge” tense by connecting some past event or state to the present moment. It implies that whatever is being referred to in the past is still true and relevant today.

“There have been several investigations into…” “Educators have always been interested in student learning.”

Some studies have shown that girls have significantly higher fears than boys after trauma (Pfefferbaum et al., 1999; Pine &; Cohen, 2002; Shaw, 2003). Other studies have found no gender differences (Rahav and Ronen, 1994). (Psychology)

Special notes

Can i change tenses.

Yes. English is a language that uses many verb tenses at the same time. The key is choosing the verb tense that is appropriate for what you’re trying to convey.

What’s the difference between present simple and past simple for reporting research results?

• Past simple limits your claims to the results of your own study. E.g., “Our study found that teenagers were moody.” (In this study, teenagers were moody.)
• Present simple elevates your claim to a generalization. E.g., “Our study found that teenagers are moody.” (Teenagers are always moody.)

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Biber, Douglas. 1999. Longman Grammar of Spoken and Written English . New York: Longman.

Hawes, Thomas, and Sarah Thomas. 1997. “Tense Choices in Citations.” Research into the Teaching of English 31 (3): 393-414.

Hinkel, Eli. 2004. Teaching Academic ESL Writing: Practical Techniques in Vocabulary and Grammar . Mahwah, NJ: Lawrence Erlbaum Associates.

Penrose, Ann, and Steven Katz. 2004. Writing in the Sciences: Exploring the Conventions of Scientific Discourse , 2nd ed. New York: Longman.

Swales, John, and Christine B. Feak. 2004. Academic Writing for Graduate Students: Essential Tasks and Skills , 2nd ed. Ann Arbor: University of Michigan Press.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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• Verb Tenses in Academic Writing | Rules, Differences & Examples

Verb Tenses in Academic Writing | Rules, Differences & Examples

Published on 20 October 2022 by Shane Bryson . Revised on 11 September 2023.

Tense communicates an event’s location in time. The different tenses are identified by their associated verb forms. There are three main verb tenses: past ,  present , and  future .

In English, each of these tenses can take four main aspects:  simple ,  perfect ,  continuous  (also known as  progressive ), and  perfect continuous . The perfect aspect is formed using the verb  to have , while the continuous aspect is formed using the verb  to be .

In academic writing , the most commonly used tenses are the  present simple , the  past simple , and the  present perfect .

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Table of contents

Tenses and their functions, when to use the present simple, when to use the past simple, when to use the present perfect, when to use other tenses.

The table below gives an overview of some of the basic functions of tenses and aspects. Tenses locate an event in time, while aspects communicate durations and relationships between events that happen at different times.

It can be difficult to pick the right verb tenses and use them consistently. If you struggle with verb tenses in your thesis or dissertation , you could consider using a thesis proofreading service .

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The present simple is the most commonly used tense in academic writing, so if in doubt, this should be your default choice of tense. There are two main situations where you always need to use the present tense.

Describing facts, generalisations, and explanations

Facts that are always true do not need to be located in a specific time, so they are stated in the present simple. You might state these types of facts when giving background information in your introduction .

• The Eiffel tower  is in Paris.
• Light  travels faster than sound.

Similarly, theories and generalisations based on facts are expressed in the present simple.

• Average income differs by race and gender.
• Older people express less concern about the environment than younger people.

Explanations of terms, theories, and ideas should also be written in the present simple.

• Photosynthesis  refers to  the process by which plants  convert sunlight into chemical energy.
• According to Piketty (2013), inequality grows over time in capitalist economies.

Describing the content of a text

Things that happen within the space of a text should be treated similarly to facts and generalisations.

This applies to fictional narratives in books, films, plays, etc. Use the present simple to describe the events or actions that are your main focus; other tenses can be used to mark different times within the text itself.

• In the first novel, Harry learns he is a wizard and travels  to Hogwarts for the first time, finally escaping the constraints of the family that raised him.

The events in the first part of the sentence are the writer’s main focus, so they are described in the present tense. The second part uses the past tense to add extra information about something that happened prior to those events within the book.

When discussing and analyzing nonfiction, similarly, use the present simple to describe what the author does within the pages of the text ( argues , explains , demonstrates , etc).

• In The History of Sexuality , Foucault asserts that sexual identity is a modern invention.
• Paglia (1993) critiques Foucault’s theory.

This rule also applies when you are describing what you do in your own text. When summarising the research in your abstract , describing your objectives, or giving an overview of the  dissertation structure in your introduction, the present simple is the best choice of tense.

• This research  aims to synthesise the two theories.
• Chapter 3 explains  the methodology and discusses ethical issues.
• The paper  concludes with recommendations for further research.

The past simple should be used to describe completed actions and events, including steps in the research process and historical background information.

Reporting research steps

Whether you are referring to your own research or someone else’s, use the past simple to report specific steps in the research process that have been completed.

• Olden (2017) recruited 17 participants for the study.
• We transcribed and coded the interviews before analyzing the results.

The past simple is also the most appropriate choice for reporting the results of your research.

• All of the focus group participants agreed  that the new version  was an improvement.
• We  found a positive correlation between the variables, but it  was not as strong as we  hypothesised .

Describing historical events

Background information about events that took place in the past should also be described in the past simple tense.

• James Joyce  pioneered the modernist use of stream of consciousness.
• Donald Trump’s election in 2016  contradicted the predictions of commentators.

The present perfect is used mainly to describe past research that took place over an unspecified time period. You can also use it to create a connection between the findings of past research and your own work.

Summarising previous work

When summarising a whole body of research or describing the history of an ongoing debate, use the present perfect.

• Many researchers  have investigated the effects of poverty on health.
• Studies  have shown a link between cancer and red meat consumption.
• Identity politics has been a topic of heated debate since the 1960s.
• The problem of free will  has vexed philosophers for centuries.

Similarly, when mentioning research that took place over an unspecified time period in the past (as opposed to a specific step or outcome of that research), use the present perfect instead of the past tense.

• Green et al.  have conducted extensive research on the ecological effects of wolf reintroduction.

Emphasising the present relevance of previous work

When describing the outcomes of past research with verbs like fi nd ,  discover or demonstrate , you can use either the past simple or the present perfect.

The present perfect is a good choice to emphasise the continuing relevance of a piece of research and its consequences for your own work. It implies that the current research will build on, follow from, or respond to what previous researchers have done.

• Smith (2015) has found that younger drivers are involved in more traffic accidents than older drivers, but more research is required to make effective policy recommendations.
• As Monbiot (2013)  has shown , ecological change is closely linked to social and political processes.

Note, however, that the facts and generalisations that emerge from past research are reported in the present simple.

While the above are the most commonly used tenses in academic writing, there are many cases where you’ll use other tenses to make distinctions between times.

Future simple

The future simple is used for making predictions or stating intentions. You can use it in a research proposal  to describe what you intend to do.

It is also sometimes used for making predictions and stating hypotheses . Take care, though, to avoid making statements about the future that imply a high level of certainty. It’s often a better choice to use other verbs like  expect ,  predict,  and  assume to make more cautious statements.

• There  will be a strong positive correlation.
• We  expect  to find a strong positive correlation.
• H1  predicts a strong positive correlation.

Similarly, when discussing the future implications of your research, rather than making statements with will,  try to use other verbs or modal verbs that imply possibility ( can ,  could ,  may ,  might ).

• These findings  will influence  future approaches to the topic.
• These findings  could influence future approaches to the topic.

Present, past, and future continuous

The continuous aspect is not commonly used in academic writing. It tends to convey an informal tone, and in most cases, the present simple or present perfect is a better choice.

• Some scholars are suggesting that mainstream economic paradigms are no longer adequate.
• Some scholars suggest   that mainstream economic paradigms are no longer adequate.
• Some scholars have suggested   that mainstream economic paradigms are no longer adequate.

However, in certain types of academic writing, such as literary and historical studies, the continuous aspect might be used in narrative descriptions or accounts of past events. It is often useful for positioning events in relation to one another.

• While Harry is traveling to Hogwarts for the first time, he meets many of the characters who will become central to the narrative.
• The country was still recovering from the recession when Donald Trump was elected.

Past perfect

Similarly, the past perfect is not commonly used, except in disciplines that require making fine distinctions between different points in the past or different points in a narrative’s plot.

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

Bryson, S. (2023, September 11). Verb Tenses in Academic Writing | Rules, Differences & Examples. Scribbr. Retrieved 7 June 2024, from https://www.scribbr.co.uk/english-language/verb-tenses/

Aarts, B. (2011).  Oxford modern English grammar . Oxford University Press.

Butterfield, J. (Ed.). (2015).  Fowler’s dictionary of modern English usage  (4th ed.). Oxford University Press.

Garner, B. A. (2016).  Garner’s modern English usage (4th ed.). Oxford University Press.

Is this article helpful?

Shane Bryson

Shane finished his master's degree in English literature in 2013 and has been working as a writing tutor and editor since 2009. He began proofreading and editing essays with Scribbr in early summer, 2014.

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Grammar: Verb Tenses

Most common verb tenses in academic writing.

According to corpus research, in academic writing, the three tenses used the most often are the simple present , the simple past , and the present perfect (Biber et al., 1999; Caplan, 2012). The next most common tense for capstone writers is the future ; the doctoral study/dissertation proposal at Walden is written in this tense for a study that will be conducted in the future.

Biber, D., Johansson, S., Leech, G., Conrad, S., & Finegan, E. (1999). Longman grammar of written and spoken English . Pearson. https://doi.org/10.1162/089120101300346831

Caplan, N. A. (2012). Grammar choices for graduate and professional writers . University of Michigan Press.

Simple present: Use the simple present to describe a general truth or a habitual action. This tense indicates that the statement is generally true in the past, present, and future.

• Example: The hospital admits patients whether or not they have proof of insurance.

Simple past : Use the simple past tense to describe a completed action that took place at a specific point in the past (e.g., last year, 1 hour ago, last Sunday). In the example below, the specific point of time in the past is 1998.

• Example: Zimbardo (1998) researched many aspects of social psychology.

Present perfect: Use the present perfect to indicate an action that occurred at a nonspecific time in the past. This action has relevance in the present. The present perfect is also sometimes used to introduce background information in a paragraph. After the first sentence, the tense shifts to the simple past.

• Example: Numerous researchers have used this method.
• Example: Many researchers have studied how small business owners can be successful beyond the initial few years in business. They found common themes among the small business owners.

Future: Use the future to describe an action that will take place at a particular point in the future (at Walden, this is used especially when writing a proposal for a doctoral capstone study).

• Example: I will conduct semistructured interviews.

Keep in mind that verb tenses should be adjusted after the proposal after the research has been completed. See this blog post about Revising the Proposal for the Final Capstone Document for more information.

APA Style Guidelines on Verb Tense

APA calls for consistency and accuracy in verb tense usage (see APA 7, Section 4.12 and Table 4.1). In other words, avoid unnecessary shifts in verb tense within a paragraph or in adjacent paragraphs to help ensure smooth expression.

• Use the past tense (e.g., researchers presented ) or the present perfect (e.g., researchers have presented ) for the literature review and the description of the procedure if discussing past events.
• Use the past tense to describe the results (e.g., test scores improved significantly).
• Use the present tense to discuss implications of the results and present conclusions (e.g., the results of the study show …).

When explaining what an author or researcher wrote or did, use the past tense.

• Patterson (2012) presented, found, stated, discovered…

However, there can be a shift to the present tense if the research findings still hold true:

• King (2010) found  that revising a document three times improves the final grade.
• Smith (2016) discovered that the treatment is effective.

Verb Tense Guidelines When Referring to the Document Itself

To preview what is coming in the document or to explain what is happening at that moment in the document, use the present or future tense:

• In this study, I will describe …
• In this study, I describe …
• In the next chapter, I will discuss …
• In the next chapter, I discuss …

To refer back to information already covered, such as summaries of discussions that have already taken place or conclusions to chapters/sections, use the past tense:

• Chapter 1 contained my original discussion of the research questions.
• In summary, in this section, I presented information on…

Simple Past Versus the Present Perfect

Rules for the use of the present perfect differ slightly in British and American English. Researchers have also found that among American English writers, sometimes individual preferences dictate whether the simple past or the present perfect is used. In other words, one American English writer may choose the simple past in a place where another American English writer may choose the present perfect.

Keep in mind, however, that the simple past is used for a completed action.  It often is used with signal words or phrases such as "yesterday," "last week," "1 year ago," or "in 2015" to indicate the specific time in the past when the action took place.

• I went to China in 2010 .
• He completed the employee performance reviews last month .

The present perfect focuses more on an action that occurred without focusing on the specific time it happened. Note that the specific time is not given, just that the action has occurred.

• I have travelled to China.

The present perfect focuses more on the result of the action.

• He has completed the employee performance reviews.

The present perfect is often used with signal words such as "since," "already," "just," "until now," "(not) yet," "so far," "ever," "lately," or "recently."

• I have already travelled to China.
• He has recently completed the employee performance reviews.
• Researchers have used this method since it was developed.

Summary of English Verb Tenses

The 12 main tenses:

• Simple present : She writes every day.
• Present progressive: She is writing right now.
• Simple past : She wrote last night.
• Past progressive: She was writing when he called.
• Simple future : She will write tomorrow.
• Future progressive: She will be writing when you arrive.
• Present perfect : She has written Chapter 1.
• Present perfect progressive: She has been writing for 2 hours.
• Past perfect: She had written Chapter 3 before she started Chapter 4.
• Past perfect progressive: She had been writing for 2 hours before her friends arrived.
• Future perfect: She will have written Chapter 4 before she writes Chapter 5.
• Future perfect progressive: She will have been writing for 2 hours by the time her friends come over.

Conditionals:

Zero conditional (general truths/general habits).

• Example: If I have time, I write every day.

First conditional (possible or likely things in the future).

• Example: If I have time, I will write every day.

Second conditional (impossible things in the present/unlikely in the future).

• Example : If I had time, I would write every day.

Third conditional (things that did not happen in the past and their imaginary results)

• Example : If I had had time, I would have written every day.

Subjunctive : This form is sometimes used in that -clauses that are the object of certain verbs or follow certain adjectives. The form of the subjective is the simple form of the verb. It is the same for all persons and number.

• Example : I recommend that he study every day.
• Example: It is important that everyone set a writing schedule.

Verbs Video Playlist

Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

• Grammar for Academic Writers: Common Verb Tenses in Academic Writing (video transcript)
• Grammar for Academic Writers: Verb Tense Consistency (video transcript)
• Grammar for Academic Writers: Advanced Subject–Verb Agreement (video transcript)
• Mastering the Mechanics: Helping Verbs (video transcript)
• Mastering the Mechanics: Past Tense (video transcript)
• Mastering the Mechanics: Present Tense (video transcript)
• Mastering the Mechanics: Future Tense (video transcript)

Related Resources

Knowledge Check: Verb Tenses

Didn't find what you need? Email us at [email protected] .

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English for Uni

Tenses in Academic Writing

Ms parrot: you've got talent.

View the video, then try the tenses exercises to test your knowledge! Watch the whole story, or see sections of the story below . All the videos have captions that you can view on YouTube.

View the video on Chinese channel youku .

View the individual video chapters

To view the individual chapters of the above video, you can either click the 'PLAYLIST' menu item in the above YouTube video and select the chapter from there, or, you can click one of the pictures below and view the individual video on YouTube. All the videos have captions that you can view on YouTube.

• You've Got Talent - The TV Show
• You've Got Talent - Teaching on Tenses
• Exercise 1: Present simple, past simple or present perfect
• Exercise 2: Present simple, past simple or present perfect
• Exercise 3: Explaining tense usage
• Exercise 4: Present simple or present perfect
• Exercise 5: Verbs and actions
• Exercise 6: Subject and verb agreement

You can view the video transcript here. You can also view the information on this page as a PDF or as a Word document and download the exercises as a PDF or as a Word document. And remember that you can see the video from China on youku .

Tenses in different languages Teachers' notes

Using tenses

Writers use tenses to give a particular meaning. The writer might be trying to emphasise the currency, regularity or even ownership of an idea. Some tenses are therefore used more than others in academic English.

The simple tenses are used for events which are immediate facts (or strong predictions in the future), complete or unchanging.

The perfect tenses are used to show the prior nature of an event in relation to some other point in time.

The continuous/progressive tenses are used for an event which is not complete, or is temporary or changing. (N.B. The word 'continuous' is used in British and Australian grammar books, while US grammar books use the word 'progressive' for these tenses.)

Here is a timeline to show you some tenses commonly used in academic writing:

Simple and perfect, present and past tenses are used most in academic writing. How many tenses did you spot in the video?

Present simple

- A general principle Talent shows usually allow people to demonstrate their creative abilities.

- Someone's opinion Professor Grahamarian thinks that talent shows are important.

- The results of an experiment The judges' scores show that X is the clear winner.

- A habitual action in the present People nearly always feel nervous before they perform on stage.

- A theory Bobby Dylan's theory about talent contests is that one should not judge by appearances.

- A claim Bobby Dylan claims that talent shows are the best way for people to become famous.

- A fact Talent shows are exciting.

Past simple

- What happened There were two acts. Prince Wolfgang won the competition.

- What happened in the past but is different now In the past, shows were simpler.

- Ideas that were held in the past but are no longer held People thought that talent contests were bad.

Used to is also used to compare the past to the present

People used to win smaller prizes. [This implies that the situation has changed.]

Present perfect

- Something that has changed over time Recently, talent contests have become bigger and more glamorous.

- Recent research and its present impact Recently, research has shown that such talent contests can make people nervous.

Tenses less used in academic writing

Generally, academic writing is based on research or theories that already exist, have already happened, or refer to a current event or view. This means that the various forms of the future are less likely to be used in essays, although they might be used in reports.

- ‘going to' future: They are going to research this next year.

- future simple: They will research this next year.

- future continuous:  They will be playing some music by Mozart.

- future perfect:  At the end of August they will have had over 150 rehearsals.

The continuous/progressive forms are less common in academic writing because they indicate something temporary.

- present continuous/progressive: They are researching this now.

- present perfect continuous/progressive: They have been researching this for many years.

- past perfect continuous/progressive: They had been researching this for many years before they found any significant results.

- future perfect continuous/progressive: By 2025, they will have been researching this for eleven years.

The past perfect situates one activity in the past before another activity. It is sometimes used in academic writing.

- past perfect: They had researched this for many years before they found any significant results.

The difference between the past perfect and the past perfect continuous is that the continuous form puts more emphasis on the length of the activity.

Stative verbs

Some verbs are called ‘stative verbs'. These are verbs that refer to states or preferences rather than to physical actions. They are not normally used in continuous or progressive forms. Here are some examples:

e.g. We say They agree rather than They are agreeing.

(Visit the Perfect English Grammar website for a comprehensive list of stative verbs and explanations about how to use them.)

Consistency of verb tenses

Jumping from tense to tense can make reading difficult and confusing. Within a sentence there needs to be a logical sequence to the ideas or events. From sentence to sentence within a paragraph there also needs to be a logical relationship.

Incorrect: The study ended suddenly because many of the participants have moved away.

This is confusing because the writer starts by looking back at the past ( ended ) but then changes their perspective to the present perfect ( have moved ).

Correct: The study ended suddenly because many of the participants had moved away.

This is better, because all the action is situated in the past.

Future tenses

It is sometimes hard to choose which future tense to use in English, as the differences in meaning can be very small. For example, the future continuous is more informal than the future simple. Here are some examples:

They will leave tomorrow. (prediction)

They leave tomorrow. (formal)

They are leaving tomorrow. (planned event; arrangements already made)

They are going to leave tomorrow. (planned event; focuses on intention)

They will be leaving tomorrow. (less definite; informal)

They will have left before you arrive tomorrow. (situates the event before another event)

Verb tenses when citing sources

When you are reporting on research in the past, then a past tense is mostly used. e.g. According to the findings by Henderson (2008), all the interviewees reported negative side effects from the drug. However, in a literature review or a paragraph that is trying to stress that certain ideas are still relevant, the present perfect tense may be used.

e.g. Even though linguists have argued that language learning is an incremental process, young children have shown that risk taking and game playing speed up the process (Chen 2009; Brown 2003).

In addition, if research results are still meaningful then the present simple is used. e.g. The data collected from Green's (2004) research suggest that exposure to stress reduces language fluency. (N.B. data can be either singular or plural.)

Subject/verb agreement

Present simple. All the verb endings are the same in regular verbs, except in the third person singular ( he/she/it ). Verbs after he/she/it add - s or -es .

Most verbs in the present simple add - s in the third person .

-es is added to verbs ending in - o , -ch , -sh , - ss , - tch , - x and -z .

Verbs ending in a consonant + y (e.g. deny , rely ) change the y to an i and add -es .

When there are several nouns, check which is the subject of the verb to see how it agrees. The subject may not be the word closest to the verb.

e.g. The use of these procedures is recommended by the university. (In this example, it is the word use which is the subject.

e.g. Recent discoveries in the area have led to an improvement in treatment.

Noun + noun = plural verb

e.g. A girl and a dog were sitting on the library steps.

Difficult cases - The number of + singular verb. The number of cases is declining.

- A number of + plural verb. A number of people are waiting for their results.

There is/There are Turn the sentence round to see if you need there is or there are .

e.g. There is an excellent article in this journal = An excellent article is in this journal.

e.g. There are some excellent articles in this journal = Some excellent article s are in this journal.

Aspect - for advanced grammar lovers

The word ‘tense' is often used to cover the two terms ‘tense' and ‘aspect'. ‘Tense' refers to the time when something happens (present, past or future). ‘Aspect' refers to how we experience the action (as something which is complete, ongoing or before another action). In English we usually refer to three aspects: simple, perfect and progressive/continuous.

The following chart is the same as the chart at the top of this page, but includes aspect as a separate category.

The simple aspect is used for facts or strong predictions.

I work in the maths department.

I cycle to the university every day.

I have an exam next week.

I submitted my assignment last week.

I will study engineering next year.

The perfect aspect is used to indicate that something happened before another point in time and is still relevant at that point in time.

I have submitteed my report.

She had left before we arrived.

He will have graduated before we even enrol.

The progressive aspect is used for an event which is not complete, or is temporary or changing.

He is busy entering the data. (= at the moment)

They are getting harder to approach.

She was working on her literature review all night.

He will be leaving at 10 am tomorrow.

A lot of the information on this page is based on Celce-Murcia, M., & Larsen-Freeman, D. (1999). The grammar book (2nd ed). US: Heinle & Heinle.

Writing academically: Tenses

• Academic style
• Personal pronouns
• Contractions
• Abbreviations
• Signposting
• Paragraph structure
• Using sources in your writing

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“Quote” Author, Book

In simple terms, verb tenses refer to the past, present or future. Verb tenses tell the reader when something happened, and are used to convey what is or is not known at the time of writing.

If you are reporting on your own or others’ specific research activities (such as methods that were used, or results that were found) then you would generally use the past tense.

• An experiment was conducted…
• It was found that…
• Brown (2010) found that…

In the example based on Brown’s research, the writer was referring to a specific result that Brown found when he conducted his research in 2010, and is therefore written in the past tense.

Present tense

Use the present tense if you are making general statements that draw on previous research, and usually indicates what is known at the time of writing , for example:

Example: 

• The research shows...
• The results of the experiment suggest that…
• Brown’s (2010) study suggests that…

In the above example based on Brown’s research, the writer makes a reference to what is known at the time of writing , and so it is written in the present tense.

Here is an example of using both the past and present tense in your writing:

Example:  Brown (2010) conducted a survey of 1000 students. The results of his survey suggest that all his students are geniuses.

In this example the writer refers to a specific survey that Brown conducted (past tense) in 2010. The writer then conveys how the results of Brown’s survey are still considered worthy today by writing in the present tense ( suggest ).

Compare this to the following example:

Example:  Brown (2010) conducted a survey of 1000 students. The results of his survey suggested that all his students were geniuses, but his later work (Brown, 2015) suggests this is not the case.

As previously, Brown’s specific study of 2010 is referred to in the past tense. But we now find that a later study by Brown (2015) appears to be in disagreement with his earlier 2010 study. Consequently the writer now refers to the conclusions drawn from the 2010 study in the past tense ( suggested ), and it is the results of Brown’s 2015 study – which represents what is known at the time of writing - that is referred to in the present tense ( suggests ).

Future tense

The future tense is not often used in academic writing because it tends to imply a level of certainty that academics can find uncomfortable. If you use verbs such as will or shall then be certain of your certainty! Otherwise use verbs that express possibility, such as could or may .

The exception is in research proposals where you are writing about what you intend to do - so the future tense is used.

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• Writing Tips

Common Issues with Tenses

4-minute read

• 6th June 2022

Using verbs correctly is one of the trickiest parts of writing . Anyone can make mistakes with verb tenses, but it’s especially easy for those who aren’t native English speakers. In this post, we’ll explain the nine main tenses and highlight the most common mistakes writers make when using them. So, read on if you want to make verb tense mistakes a thing of the past!

What Are the Main Tenses in English?

Every action happens in the past, present, or future. Each of these time frames is further divided into the simple , continuous, or perfect form. Here’s an example of each:

●  Simple past – things that happened before now:

I wrote an essay last week.

●  Past continuous – an ongoing action in the past:

He was writing a poem yesterday morning.

●  Past perfect – an action that ended before a point in the past:

By lunchtime, he had written six lines.

●  Simple present – a habitual action:

She writes at her desk by the window.

The simple present is also used to describe actions happening at this moment:

I want a desk like that.

●  Present continuous – an ongoing action happening right now:

I am writing a future bestseller!

●  Present perfect – an action that began in the past and is still happening now, or one that happened at an unspecified time:

He has written stories since he was a child.

I have written 1000 birthday cards.

●  Simple future – things that’ll happen and then stop happening:

I will read the first chapter of the book tomorrow.

●  Future continuous – things that’ll begin in the future and continue for some time:

I will be writing a book report.

●  Future perfect – an action that’ll end at some point in the future:

I will have written it by the end of the week.

With so many tenses to choose from, it’s no wonder people make mistakes. Don’t worry, though, because for most academic writing, you don’t need to use all of them. Essays and assignments are nearly always written in the simple present tense, and if you’re describing your own research methodology (e.g., an experiment or survey), you would use the simple past tense.

What Are the Most Common Verb Tense Errors?

Mistakes with verb tenses usually fall into one of three categories:

• Changing from one tense to another.
• Overusing continuous tenses.
• Confusion with irregular verbs.

Use tenses consistently

Your readers will get confused if you switch tenses unexpectedly:

Find this useful?

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The car drove into the tunnel, and it comes out the other end. ✘

The car drove into the tunnel, and it came out the other end. ✔

The car drives into the tunnel, and it comes out the other end. ✔

This doesn’t mean changing tense mid-sentence is always wrong. But make sure you’re saying what you intend to say!

I practiced using different tenses, and now I understand them better. ✔

Limit your use of continuous tenses

Your writing can easily become quite clunky if you use a lot of continuous verb forms:

It was pouring rain while we were camping, and the children were complaining because their blankets were getting wet.

It poured rain while we were camping, and the children complained because their blankets got wet.

The first sentence contains four present participles (i.e., verb forms that end in ing ), which makes it quite a chore to read and rather repetitive. In the second version, we’ve replaced three of them with the simple past tense. This makes the writing more concise and easier to read.

Watch out for irregular verbs

We form the simple past tense and the past participle of most verbs by simply adding ed to the base verb (e.g., walk – walked; open – opened ). However, there are many verbs that don’t obey such rules, and we call these irregular verbs . Unfortunately, there’s no easy way to learn how to conjugate irregular verbs because they don’t follow an obvious pattern, as these examples show:

I buyed bought a gigantic jar of honey.

It costed cost $10.

I hided hid it in the back of the cupboard.

I soon forgetted forgot all about it.

As you read English texts and listen to people speaking in conversation, you’ll recognize more irregular verbs and become familiar with how they work.

Proofreading for Perfect Grammar

We hope you now feel confident about using different tenses in your writing. If you’d like an expert to check your work for incorrect verbs and any other mistakes in grammar, spelling, or punctuation, our proofreaders are here to help. Send us a free trial document to find out more.

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Tenses in writing

Verb tenses.

The present tense is used to express anything that is happening now or occurring in the present moment. The present also communicates actions that are ongoing, constant, or habitual. For example:

Use the past tense to indicate past events, prior conditions, or completed processes. For example:

The future tense indicates actions or events that will happen in the future. For example:

Aspect allows you to be more precise in your selection of verbs. Aspect falls into two categories: continuous and perfect. To indicate the continuous aspect, add a form of the verb "to be" and a present participle to your main verb. The perfect aspect is created with a form of the verb "to have" and a past participle. The following chart shows twelve forms of the verb "to write" that result from combining time with aspect.

( aspect summary )

A participle is a verb form that can be used as an adjective.

Aspect in Detail

The continuous aspect is created with a form of "to be" and a present participle ( about participles ). For example:

The perfect aspect is created with a form of the verb "to have" and a past participle. For example:

The perfect aspect is often the most challenging to understand, so here's a brief overview.

Past Perfect describes a past action completed before another. For example, the next two sentences describe one action followed by another, but each achieves a different rhetorical effect by using different verb forms.

"Wrote" and "reread" sound equally important in the first sentence. In the second, the past perfect form "had written" emphasizes the action "reread."

Present Perfect refers to completed actions which endure to the present or whose effects are still relevant.

Future Perfect refers to an action that will be completed in the future.

One final note: the terms used to describe aspect have changed over time, and different terms are often used to describe the same aspect. It may help to know that the following terms are equivalent:

• "simple present" (or) "present indefinite"
• "past continuous" (or) "past progressive" (or) "past imperfect"
• "past complete" (or) "past perfect"
• "past perfect continuous" (or) "past perfect progressive"

Verb Tenses in Context

Conventions governing the use of tenses in academic writing differ somewhat from ordinary usage. Below we cover the guidelines for verb tenses in a variety of genres.

Academic Writing

• Books, Plays, Poems, Movies, etc.

Historical Contrast

Research proposals, resumes and cover letters, stories/narrative prose.

1. Academic writing generally concerns writing about research. As such, your tense choices can indicate to readers the status of the research you're citing. You have several options for communicating research findings, and each has a different rhetorical effect. For example:

• 1.3 According to McMillan (1996), the most common cause of death was car accidents.

If you choose the present tense, as in Example 1.1, you're implying that the findings of the research are generally accepted, whereas the present perfect tense in 1.2 implies not only general acceptance but also current relevance and, possibly, the continuity of the findings as an authoritative statement on the causes of death. On the other hand, the past tense in Example 1.3 emphasizes the finding at the time the research was conducted, rather than its current acceptance.

However, if you are writing about specific research methods, the process of research and data collection, or what happened during the research process, you will more commonly use the past tense, as you would normally use in conversation. The reason is that, in this instance, you are not emphasizing the findings of the research or its significance, but talking about events that occurred in the past. Here is an example:

• 1.4 During the data collection process, Quirk conducted 27 interviews with students in his class. Prior to the interviews, the students responded to a brief questionnaire.

Books, Poems, Plays, Movies

2. When you are discussing a book, poem, movie, play, or song the convention in disciplines within the humanities is to use the present tense, as in:

• 2.1 In An Introduction to English Grammar (2006), Noam Chomsky discusses several types of syntactic structures.
• 2.2 In Paradise Lost , Milton sets up Satan as a hero who changes the course of history.

3. In cases where it is useful to contrast different ideas that originate from different periods , you can use the past and the present or present perfect tense to do so. The past tense implies that an idea or a theory has lost its currency or validity, while the present tense conveys relevance or the current state of acceptance.

For example, when you want to discuss the fact that a theory or interpretation has been supplanted by new perspectives on the subject:

• 3.1 Stanley Fish (1993) maintained a reader-response stance in his analysis of Milton's L'Allegro and Il Penseroso . However, recent literary critics consider/have considered this stance to be inappropriate for the two poems.

The verb tenses used above emphasize the contrast between the old view (by Stanley Fish), which is indicated by the past tense, and the new view (by "recent literary critics"), which is indicated by the present tense or the present perfect tense. The difference between the present tense and the present perfect (i.e. between consider and have considered ) is that the present perfect suggests that the current view has been held for some time.

4. The future tense is standard in research proposals because they largely focus on plans for the future. However, when writing your research paper, use the past tense to discuss the data collection processes, since the development of ideas or experiments— the process of researching that brings the reader to your ultimate findings—occurred in the past.

5. In a resume, the past tense is used for reporting past experience and responsibilities. However, in a statement of purpose, a personal statement, or a cover letter, the present perfect tense is commonly used to relate past experience to present abilities, e.g., "I have managed fourteen employees."

6. The past tense is commonly used when writing a narrative or a story , as in:

• 6.1 Once upon a time, there was a peaceful kingdom in the heart of a jungle . . .

Some writers use the present tense in telling stories, a technique called the "historical present" that creates an air of vividness and immediacy. For example:

• 6.2 Yesterday when I was walking around downtown, the craziest thing happened. This guy in a suit comes up to me, and says , "If you know what's good for you . . . "

In this example, the speaker switches from the past tense in giving context for the story to the present tense in relating the events themselves.

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What Tense Should I Use in Writing?

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When writing, people are often confused about what tense they should use. Should I write this MLA history paper in past tense? Should I write my short story in present or past tense? How about a resume: should I write my job entries in present or past? And these people are right to be confused because what tense you should use varies widely depending on your writing style and your purpose.

Academic (Four Main Styles)

APA/Harvard: Per APA (and its non-American variant, Harvard), you should primarily use past tense, especially in literature reviews where you’re talking about authors’ past studies. It should be:

“Johnson (2008) argued . . .”

“Johnson (2008) argues . . . .”

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The same is true for your Results and Method sections, but APA makes an exception for Discussion sections (where you examine your conclusions and the implications of the study), which can be in present tense if it better conveys your meaning.

MLA: This style is a bit more straightforward. Per MLA, you should be almost always using present tense:

“In To Kill a Mockingbird , Atticus Finch argues . . .”

If you need to differentiate time, you should use present perfect tense:

“For many years, Scout has been worrying about . . . .”

If you must, you can use some past tense, but keep it to a minimum.

Chicago: This style is a bit more lenient. Per Chicago, you can use either present or past (Though it’s best to use present when discussing literature and past when writing about history.), but make sure you stay consistent. If you switch, make sure you need to, such as:

The Romans used various military strategies, some of which are still in use today.

AP: AP, which is used by news media, is also more flexible. There is no set tense; instead, you should be endeavoring to use present/past/future as necessary to make sure the events you are describing are as clear as possible. AP also recommends using time words (today, tomorrow, March 17, etc.) to anchor your piece and further reduce ambiguity.

When talking about your job experience in resumes, the rule is simple: Use present tense for current positions:

Lead team in HVAC solutions

And use past tense for past positions:

Led team in HVAC solutions

Business Plan

Professors and potential investors have different views on what tense a business plan should be written in, but definitely you should be using either future or present tense. Some people argue that you should always write a business plan in future tense because you’re talking about your future plans.

But there’s another school of thought that recommends using present tense instead because this will allow your plan to stay current as you develop it and you develop your business. In other words, as you develop your business, you develop your plan, and it stays current with what you’re doing.

Above all, fictional writing needs to be consistent in its tense. Just as above, don’t switch unless you must. (BTW, fictional writing is done in Chicago Style.)

Everything Else

For everything else, such as business letters, admission essays, and e-mails, and especially in more informal contexts, just use your best judgment and write in whatever tense feels right to you. Go with your instincts and remember that, unless you’re writing in a formal academic context, you have more leeway to do whatever you like.

Just remember, for all styles and purposes, always be consistent. Try to pick one tense and stick with it throughout your piece. If you have to switch tenses, make it very obvious why you are doing so, and at least try to start new paragraphs for new tenses.

That’s it, I hope you have/had/will have good luck in your writing!

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Writing tenses: 5 tips for past, present, future

Understanding how to use writing tenses is challenging. How do you mix past, present and future tense without making the reader giddy? What is the difference between ‘simple’ and ‘perfect’ tense? Read this simple guide for answers to these questions and more:

• Post author By Jordan
• 28 Comments on Writing tenses: 5 tips for past, present, future

What are the main writing tenses?

In English, we have so-called ‘simple’ and ‘perfect’ tenses in the past, present and future. The simple tense merely conveys action in the time narrated. For example:

Past (simple) tense: Sarah ran to the store. Present (simple) tense: Sarah runs to the store. Future (simple) tense: Sarah will run to the store

Perfect tense uses the different forms of the auxiliary verb ‘has’ plus the main verb to show actions that have taken place already (or will/may still take place). Here’s the above example sentence in each tense, in perfect form:

Past perfect: Sarah had run to the store. Present perfect: Sarah has run to the store. Future perfect: Sarah will have run to the store.

In the past perfect, Sarah’s run is an earlier event in a narrative past:

Sarah had run to the store many times uneventfully so she wasn’t at all prepared for what she saw that morning.

You could use the future perfect tense to show that Sarah’s plans will not impact on another event even further in the future. For example:

Sarah will have run to the store by the time you get here so we won’t be late.

(You could also say ‘Sarah will be back from the store by the time you get here so we won’t be late.’ This is a simpler option using the future tense with the infinitive ‘to be’.) Here are some tips for using the tenses in a novel:

1. Decide which writing tenses would work best for your story

The majority of novels are written using simple past tense and the third person:

She ran her usual route to the store, but as she rounded the corner she came upon a disturbing sight.

When you start drafting a novel or a scene, think about the merits of each tense. The present tense, for example, has the virtue of:

• Immediacy: The action unfolds in the same narrative moment as the reader experiences it (there is no temporal distance: Each action happens now)
• Simplicity: It’s undeniably easier to write ‘She runs her usual route to the store’ then to juggle all sorts of remote times using auxiliary verbs

Sometimes authors are especially creative in combining tense and POV. In Italo Calvino’s postmodern classic , If on a winter’s night a traveler ( 1979), the entire story is told in the present tense, in the second person. This has the effect of a ‘choose-your-own-adventure’ novel. To rewrite Sarah’s story in the same tense and POV:

You run your usual route to the store, but as you round the corner you come upon a disturbing sight.

This tense choice is smart for Calvino’s novel since it increases the puzzling nature of the story. In If on a winter’s night a traveler , you, the reader, are a character who buys Calvino’s novel If on a winter’s night a traveler , only to discover that there are pages missing. When you attempt to return it, you get sent on a wild goose chase after the book you want.

Tense itself can enliven an element of your story’s narration. In a thriller novel, for example, you can write tense scenes in first person, present tense for a sense of danger unfolding now . Tweet This

A muffled shot. He sits up in bed, tensed and listening. Can’t hear much other than the wind scraping branches along the gutter.

2. Avoid losing clarity when mixing tenses

Because stories show us chains and sequences of events, often we need to jump back and forth between earlier and present scenes and times. This is especially true in novels where characters’ memories form a crucial part of the narrative.

It’s confusing when an author changes tense in the middle of a scene. The fragmented break in continuity makes it hard to place actions in relation to each other. For example:

Sarah runs her usual route to the store. As she turned the corner, she came upon a disturbing scene.

This is wrong because the verbs do not consistently use the same tense , even though it is clear (from context) that Sarah’s run is a continuous action in a single scene.

Ursula K. Le Guin offers excellent advice on mixing past and present in her writing manual, Steering the Craft :

It is highly probable that if you go back and forth between past and present tense, if you switch the tense of your narrative frequently and without some kind of signal (a line break, a dingbat,a new chapter) your reader will get all mixed up as to what happened before what and what’s happening after which and when we are, or were, at the moment. Ursula K. Le Guin, Steering the Craft

In short, make sure there are clear breaks between sections set in different tenses and that actions in the same timeline don’t create confusion by using different tenses for the same scene’s continuous events.

These 10 exercises for practicing tenses provide a fun way to focus on mastering the basics.

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3: Mix the tenses for colour and variety

Le Guin raises a good point about writing tenses. Le Guin describes the downside of telling a story almost exclusively in present tense:

It all rather sounds alike…it’s bland, predictable, risk-free. All too often, it’s McProse. The wealth and complexity of our verb forms is part of the color of the language. Using only one tense is like having a whole set of oil paints and using only pink. Le Guin, Steering the Craft

Instead mix different tenses where appropriate, but signal changes between time settings:

For example:

That morning, she had run her usual route to the store. As she turned the corner, she had come upon a disturbing scene. Apart from the glass and metal sprayed across the road like some outgoing tide’s deposit, there were what looked like two stretchers, mostly eclipsed from view by a swarm of emergency workers. Now, safely home, she decided to lie down, all the while trying to get that scene out of her mind.

Mixing the tenses can help to show the cause and effect of interlocking events. The use of the past perfect to describe the scene of an accident in the example above is effective because the past perfect shows what is already complete. It gives it an irrevocable quality, the quality of a haunting, living-on-in-memory event. Finished, but not finished in the character’s mind’s eye.

4. Practice showing shadowy past or present actions using verb forms

In addition to simple and perfect tenses, there are different ‘moods’ that show verbs as hypothetical or possible actions. In addition to the indicative mood (‘she runs to the store’) there is also the subjunctive mood (‘If she runs to the store’) and the potential mood (‘she may run to the store’).

The different moods are useful because they can show possibilities and scenarios that might have happened, or might still happen, under different circumstances. Here are examples for correct uses for each of the tenses (in active voice):

Subjunctive mood:

Present tense: If she runs to the store… Past tense: If she ran to the store… Future tense: If she should run to the store… Present perfect tense: If she has run to the store… Past perfect tense: If she had run to the store… Future perfect tense: If she should have run to the store….

Think of this mood as setting up a possibility. For example: ‘If she runs to the store, she better be quick because we’re leaving in 5.’

The potential mood helps us show shadowy, more hypothetical, uncertain scenarios:

Present tense: She may run to the store. Present perfect tense: She may have run to the store. Past perfect: She might have run to the store.

In each of these examples, the action is a possibility and the mood (using the various forms of ‘may’) shows this.

These verb moods in conjunction with tense are useful. They help us describe situations in which a narrator or character does not have full knowledge of events, or is wondering how events might pan out. They help to build suspense in the build-up to finishing a book .

5. Practice rewriting paragraphs in different tenses

It’s often easiest to get the hang of tense by doing. Pick a paragraph by an author and rewrite in each of the tenses. Here, for example, is a paragraph from David Sedaris’ essay, ‘Buddy, Can you Spare a Tie?’:

The only expensive thing I actually wear is a navy blue cashmere sweater. It cost four hundred dollars and looks like it was wrestled from the mouth of a tiger. “What a shame,” the dry cleaner said the first time I brought it in. The sweater had been folded into a loaf-sized bundle, and she stroked it, the way you might a freshly dead rabbit. David Sedaris, ‘Buddy, Can you Spare a Tie?’ , When You Are Engulfed in Flames

Rewritten in past simple tense:

The only expensive thing I actually wore was a navy blue cashmere sweater. It cost four hundred dollars and looked like it was wrestled from the mouth of a tiger. “What a shame,” the dry cleaner said the first time I brought it in. The sweater was folded into a loaf-sized bundle, and she stroked it, the way you might a freshly dead rabbit.’

Here is the same passage in past perfect:

The only expensive thing I had actually worn was a navy blue cashmere sweater. It had cost four hundred dollars and had looked like it had been wrestled from the mouth of a tiger. “What a shame,” the dry cleaner had said, the first time I brought it in. The sweater had been folded into a loaf-sized bundle, and she had stroked it, the way you might a freshly dead rabbit.

The effect is of a character describing the defining experiences before another event (before buying an even more expensive item of clothing, for example). For example, you could write ‘Before I bought that lavish suit…’ before the paragraph.

To perfect writing tenses, make your own exercises and practice rewriting extracts from your story in each tense to see the changing effect this has on your narrative.

Do you need feedback on your use of tense in a story? Get novel help from our writing community or your own, experienced writing coach.

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• Tags how to write tense , tense and narration , writing tenses

Jordan is a writer, editor, community manager and product developer. He received his BA Honours in English Literature and his undergraduate in English Literature and Music from the University of Cape Town.

28 replies on “Writing tenses: 5 tips for past, present, future”

A fine explanation of tenses. A subject often ignored, having been overlooked except by students of language. In short, changes in tense are great aids to tension.

Thanks, Bob! It’s true that it’s not discussed as commonly as certain other topics such as characterization.

Reading such articles clear all the confusion. Thanks!

I have question though, I am writing in past tense, all the events are happening in past tense. But, say, my protagonist is in a situation where she has to decide something and she is anticipating something, in short, it’s future for her, how do we go about that.

She was still sitting on the same bench, as she didn’t want to leave the light. She was sure that ………………………………………………………………….

What I want to write here is, she knew that she will not find any cab at this hour. a. She was sure that she will not find any cab at this hour. b. She was sure that she was not going to any cab at this hour. c. She was sure that she couldn’t get a cab at this hour.

In my current scene, I am trying to show the thought process of the protagonist and I have encountered 2 or 3 places where I have come across this situation. Am I doing something wrong? Should I not come across such situation at all if I am writing in past tense?

I understand reading helps, but at this moment, my mind is blank and I am not able to recollect anything that I (must) have read.

Please suggest.

Hi Jayendra,

Thanks for your question and the feedback. Number a. would be incorrect because ‘will’ is in the simple future tense (it would be correct in ‘She is sure she will not find any cab at this hour’). B would be correct with a few small tweaks: ‘She was sure she wasn’t going to find any cab at such a late hour’ (or ‘…any cab so late at night.’) Incidentally, ‘this’ implies present, continuous time so it is a little jarring in past tense (hence the alternatives above). c. Similarly, this option would be better as ‘She was sure she wouldn’t find a cab at such a late hour.’ ‘Would not’ is the right past tense form here, in present tense it would be ‘will not’. It implies future action in relation to the present time of the narration.

I hope that helps!

Hey Bridget, thanks for your reply. It feels silly now. If I was able to come up with “could”, why couldn’t I think of “would”! 🙂

Thank you for this article. Tense has been driving me insane as it feels like there are hundreds of exceptions when it comes to usage of “simple present verbs” in past tense narratives. It makes me want to disregard the entire subject and rely on an editor to catch any mistakes that I don’t naturally leave out.

For example. When you said, “Past perfect: Sarah had run to the store.” “Run” is a present (simple) tense verb, which would make you think that it can’t be used at all in a past tense narrative, but it clearly can if you phrase it correctly. This holds true with literally dozens of other verbs, adverbs, and other “tense” related words. I’m finding my work being hampered by this as I literally stumble over myself thinking I buggered up a word in my narrative, only to later find out it was a perfectly acceptable usage. I’m really at the breaking point over this, and I’m close to just disregarding it all together and relying on pure instinct and proofreading, then review by an editor at a later date. Then of course, there’s the whole deal with acceptable tense shifting…

Am I incorrect for thinking this way? Will this kind of mindset bar me from any chance of ever getting published or even being given an offer by an agent? Is there room in this world for easily confusable writers? I don’t know, and I can’t imagine how confusing this must be for foreign speakers, either. As I’ve been speaking english all my life and writing as a hobby for nearly a decade.

Anyway, sorry for the rant. I actually do have an actual question. How do you use simple present tense usages of “being” when writing in second person past tense? Because the phrase, “You are…(whatever character’s name) comes up quite a bit. However, there’s no way to get around the fact that you have to use “are,” in the past tense continuous, and I can’t find any info on if that is correct or not.

I have a question. Would it be incorrect if my story is in first person point of view and narrated in the past tense, but the internal monologue of my narrator is in the present tense?

Ex. “Don’t you ever go anywhere else, Red?” My name isn’t Red. I can’t remember where that nickname came from. “I go to school.” I said. I could feel him rolling his eyes at me. I think he’s done that before. “Come with me today.” I looked at him then, a little puzzled. It was a bad idea and yet I said: “Okay.”

It sounds right in my head but I feel like the tenses are too all over the place to be correct. The narrator has memory problems so I want what he’s thinking to be read but I’m just not sure if this is correct. I’m more comfortable with past tense writing but should I switch to present tense?

I have the same question!

Hi Hannah, this comment slipped by, my sincere apologies for that.

Regarding your question, the tense switching does jolt the reader out of the story. If you’re more comfortable with past tense, I’d suggest putting the internal monologue in past, too. For example:

“I go to school,” I said. I could feel him rolling his eyes at me. He’d done that before.’ Similarly, for ‘I can’t remember where that nickname came form’, you could simplify it to make past tense less clunky as: ‘Where did that nickname come from?’

I hope your story is much further along now!

I’m a translator struggling with getting the past perfect correct in the story I’m working on. I find your article very helpful. Thank you 🙂

I have one question:

That morning, she had run her usual route to the store. As she turned the corner, she had come upon a disturbing scene. Apart from the glass and metal sprayed across the road like some outgoing tide’s deposit, there were what looked like two stretchers, mostly eclipsed from view by a swarm of emergency workers.

The above example sentences describe an event that had happened in the past from the narrator’s perspective, and that’s why the past perfect is used. Okay, no problem. But why isn’t everything in the past perfect? Why is it okay to leave some parts in simple past?

“As she turned the corner” instead of “As she had turned the corner” “there were what looked like two stretchers” instead of “there had been what looked two stretchers”

This is the exact issue I’m having in my story. When I put every single verb in the past perfect, the sentences sound very heavy, especially when the section describing the past event is long. But I’m not sure which parts are okay to leave in simple past.

Thank you for the feedback and for your question. You struck the exact reason there – stylistically, to put every single verb in past perfect does read clunkier and isn’t necessary. As long as there is a past-perfect verb establishing the time-frame of events, the rest of the events that are still contextually happening in the earlier time period don’t necessarily need past perfect. For example:

‘It happened last week. I had stopped by the vet shop to get my dog’s flea tablet [past perfect – prior action is established]. I was standing at the counter waiting to pay when I saw the new vet through the back entrance.’ If you wrote ‘I had been standing at the counter waiting to pay when I had seen the new vet…’ each instance of past perfect situates the action in a time period before the ‘main action’. Whereas the scene the narrator is describing is the main event unfolding after a prior action (stopping at the vet shop) situated before this encounter by past perfect tense.

There’s a useful article explaining past perfect further here: http://www.englishlessonsbrighton.co.uk/use-past-perfect-build-narratives/

Thank you so much for your quick response, Jordan! Your explanation and the link you shared are very helpful 😀

It’s a pleasure 🙂 Glad I could help! Good luck with your story.

Hi Jordan. I have a question regarding exceptions. Are there any? I’m busy writing a short and it currently starts out as “I live on the top floor of a two storey apartment complex.” I then proceed to recollect in past tense. The entire story takes place over the course of 1 night and ends with the protagonist still living there. I think – as I’m typing this out – I should probably change it to past tense right? The rest of story is written in past tense. I should treat the entire event as a recollection rather than get caught up in the fact that the protagonist is still currently living there. It just felt like I was setting it up as a “Once upon a time I lived on the top floor…” which is not really my intention. It’s part of series so “I” will still be living there. It just seemed like a nice opener using present tense. Any ideas on how I can achieve the same effect?

Thank you for sharing this interesting question. I can’t see any reason why you couldn’t begin and end on present. As long as the cuts between present and past are clear/signaled to your reader it should be fine. For example:

‘I live on the top floor of a two-storey apartment complex. You’ll know why I’ve shared this detail soon, as it connects to what I’m about to tell you about a strange event that happened two weeks ago.

I was….’

If you bookend a section in present tense this way, with a clear transition between the tenses using narration, it should be fine. The main thing with tenses is not to hop between tenses within the same narrative time-frame (for example ‘I am running down a dark street. I heard footsteps behind me.’ Here, there’s nothing to signal the passage between present and past and it’s confusing.

I hope this helps!

Hey! I’m a self-taught proofreader, not a writer myself (haven’t a creative bone in my body, sadly), and I’m having a great deal of difficulty learning present tense. Up until now, all the stories I’ve proofread have been in past tense, so I’m trying to teach myself how to correct tense errors.

However, many of the websites I’ve come across aren’t tutorials, they’re essays about why not to use present tense in fiction! Well, that’s up to the author to decide! The issue I’m having is mostly with knowing when to allow usage of past tense to go and when to correct it.

For instance, in this sentence: “Thrown by the jump in numbers, most viewers click back in the video just to double-check that Danny had indeed jumped from #3 to #6, before shrugging and continuing to watch.” I’m thinking that “had” needs to be “has”, but I’m not 100% sure. I like to be mostly sure before suggesting a change. Thanks. 🙂

Hi Tracy! Here the past perfect tense (‘had’) is acceptable because it describes an action completed before the present narrative time-frame (e.g. ‘I’m walking to the store now which had been closed this morning’ would be correct if the narrator were walking in the afternoon). If you wrote ‘I’m walking to the store now which has been closed this morning’ this would imply that it is still morning in the time of narration, due to ‘has’ here being in the present perfect tense (describing a past action or condition (‘being closed’) stretching into the present time).

‘Has’ in your example would read a little strangely as it could imply that Danny ‘has’ (in the present, continuing moment) jumped from #3 to #6.

I would say, since the video has already been recorded, that ‘had’ makes sense because Danny’s error (jumping from #3 to #6) ‘had’ been made at the time of recording, and had been viewed prior to the viewer’s realization. So both moments are squarely in the past rather than stretching into the present.

Does that make sense? 🙂 Tense will get you!

It absolutely does, thank you! I’m going to have to go back and reread certain things now, but I definitely understand this. So things that happened prior to the time frame in the story can be past tense, even in a present tense story! Thank you again, so very much, I’m trying so hard to learn this, but I just find it difficult. xD Your explanation certainly simplified it for me, though! ^_^

This post also sums up the differences very well: https://www.dailywritingtips.com/has-vs-had/

So, in short, can I use different tenses in my work of a story writing? In direct speech inverted commas are needed.Isn’t it?

MIXING PAST AND PRESENT TENSES

The following paragraph has a mixture of past and present tense. I believe it to be grammatically wrong but, to my mind, it doesn’t jar when I read it back and it gives the reader a sense of immediacy. My question is: Is it an absolute no-no or is there a degree of artist license here?

Archie flicked on the chainsaw’s master switch and pumped the primer a few times. Resting the saw on the ground he gave the cord a good hard yank. It clacked through its gears but didn’t catch. The second pull bit and snapped back stinging his fingers as it recoiled. “Son of a….” he yelped. The third pull sprung the chainsaw into life with a metallic shrill sending out a cloud of blue smoke that wafted across the laundry. Archie let it idle in a high pitched grumble and then tested it with a few pumps of the throttle that sent the chain shinning around the blade. “Seems okay” he yelled over the noise before killing the master switch. “I guess the real test will be half way through a tree.

Thank you for sharing that, there’s a great descriptive density to it and a clear sense of scene.

I’m curious as to why you think it mixes tenses? To my eyes, it’s all in past tense. You do have a participle phrase or two (e.g. ‘Resting the saw on the ground’) that provide a present/unfolding action, but these are used correctly within past tense for the overall narration (you do use it correctly to show one action that is ongoing during another – the finite verb ‘he gave…’ after that participle phrase still keeps the tense within past as expected).

It would be mixed if you had finite verbs in different tenses for events occurring in the same time-frame, e.g. ‘He rests the saw on the ground and gave the cord a good hard yank.’ This would be jarring because there would appear to be two different time-frames for actions unfolding within the same scene, thanks to present verb ‘rests’ and past verb ‘gave’. I hope this helps!

Great article, many thanks!

Brief question – when writing in the past tense, can you still use present tense for general statements? For example:

I woke up as usual at 5:47 station time when air supply unit number five, that occupied the majority of the level below our quarters, sprang into action, producing a constant humming that would last for the next eight hours. It is never completely quiet on a space station, there are always sounds, vibrations and audible movements, and you learn to live with it. It never bothered me, it was the only life I knew.

Hi Stephan, it’s a pleasure. I’m glad you found it helpful.

Thank you for sharing your question. That does scan fine. In the first instance, there is a participle phrase which creates the sense of a presently unfolding action within the past time-frame (‘producing a constant humming…’). This is correct usage.

Then the flip to present informs the reader of a general, ongoing state of affairs which is where we would use present tense. It depends on the site in time from which the narrator is speaking. If they are no longer living in the quarters when narrating this, then perhaps ‘It was never completely quiet on the space station…’ would make more sense (past tense for recounting conditions no longer being experienced). But if they are still based at the station, then present tense narration for a general state of affairs in their environment fits, as presumably it still isn’t ever completely quiet when they’re narrating this.

I hope this helps! Thanks for the great question.

Thank you for this article. I found it helpful. Both of my main characters at one point recall their dreams. Since they are recalling them, I would write them in past tense correct?

Hi Chelsea, it’s a pleasure! Not necessarily. I find authors often use present tense for this (especially if the main narration is in past tense). It would look something like:

But then I remembered the dream I had…

I’m standing in a wide, open field. I hear someone calling from the other side …

Present tense does create a sense of the unfolding moment that suits the sense of reenacting an interesting event, so personally I would lean towards that. I hope this helps. Just remember whichever tense you’re using to have a narrative link that clarifies that the narration is now crossing over into the dream description (in my example above, it’s the words ‘But then I remembered the dream I had).

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12 Tenses in English

Without tenses in English, it would be impossible for you to tell people what you want, what you did and what your plans are.

Nothing would make sense anymore.

So in this post, you will learn everything you need to know about the tenses in English—from what they are to how to practice them.

What Is a Tense?

What are the tenses in english, what do participles have to do with tenses, what is the emphatic form, why learning the tenses in english is important, how to learn english verb tenses, when should you learn the tenses in english, where to practice tenses in english, and one more thing....

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

Simply put, a tense is a form of a verb that allows us to express time.

For example, if you want to talk about something that happened last week, you would use the past tense .

If you want to talk about something that will happen next month, you would use the future tense .

When using tenses in English, we use the present (right now) as our point of reference because we are living in it.

So if you ate pizza yesterday, that action happened in the past, right?

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That’s because it happened before the present time (before right now).

Since you ate pizza in the past, if you want to tell someone about it, you will have to use the past tense in English.

I ate pizza yesterday .

Now imagine you want to tell your friend about a trip to the mountains. This trip is going to happen in three days, so the trip is in the future.

You cannot possibly use the same tense you used to say that you ate pizza yesterday.

This time, you will have to use a different tense, one that expresses that an action will happen after our present time .

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That tense is the future simple tense:

I will go on a trip next Saturday .

English has 12 tenses. They are divided into three groups (present, past and future).

Each group is further broken down into simple, continuous (progressive), perfect and perfect continuous tenses.

The Present Tense

The present tense group includes four tenses that either allow us to talk about the present or are related to it somehow.

The present simple

We use the present simple to talk about repeated actions, habits, general truths, wishes and emotions.

This tense is also used for directions and instructions, and to talk about timetables and schedules.

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She goes to school by bus.

I am very tired.

The plane lands at 7:30 a.m.

The present continuous

The present continuous is used to describe actions or events that are happening at the moment (right now) or are ongoing.

We also use this tense when someone or something is bothering us.

I am not learning anything.

Mario is reading a book.

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You are always arriving late to class!

The present perfect

This is the tense we use to describe actions and events that happened at some point in the past but have a consequence or effect on the present.

I have lost my wallet.

Mary has broken her leg.

The neighbors have just moved in.

The present perfect continuous

We use this tense to talk about actions and events that started happening in the past and are still happening today/now.

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I have been living in Poland for 13 years.

They have been waiting for over three hours.

She has been learning English for six months.

The Past Tense

The past tense includes the four tenses that help us talk about the past .

The past simple

The past simple is used to describe past actions and events that are completely finished.

He died in 2012 in a car accident.

I went to the mall last week.

Rose got married last month.

The past continuous

We use the past continuous when we need to describe past actions and events that lasted over a period of time.

I was running when I fell.

They were watching TV while I was washing the dishes.

Someone rang the bell when Sarah was having a bath.

The past perfect

We use past perfect to talk about actions and events that happened before another action or before a specific time in the past.

He had already left when I arrived.

Brooke had already moved to France when that happened.

When the police arrived, the thief had already escaped .

The past perfect continuous

We use the past perfect continuous to talk about actions and events that started in the past and lasted until another action or event in the past happened.

He had been waiting for two hours when he decided to go back home.

The kids had been playing for only five minutes when it started to rain.

By the time I arrived home, she had been crying for at least an hour .

The Future Tense

The future tense group includes the four tenses that refer to the future.

The future simple

We use the future simple mainly for predictions and spontaneous decisions.

I think it will rain tomorrow.

We will help you!

You will find the love of your life in December.

The future continuous

This tense is used to refer to actions or events that will be in progress at some point in the future.

I will be working when you come back.

Jessica will be waiting for you there.

We will be eating dinner by then.

The future perfect

We use the future perfect tense to say that an action or event will be finished at some point in the future.

He will have finished by 8 p.m.

The man will have written the report by Monday.

I will not have eaten by the time you come back.

The future perfect continuous

The future perfect continuous is used to describe actions and events that, at some point in the future, will have already lasted for some time.

By the end of this year, I will have been living in London for seven years.

She will have been waiting for three hours by 5 p.m.

In two months, I will have been studying English for one year .

Learn more about continuous tenses here: 

https://www.fluentu.com/blog/english/continuous-tense-english/

English has two special verb forms that are called English participles .

The present participle  is the participle that ends with -ing .

You have seen it in the previous section with the verb to be in the continuous compound tenses .

Here you have two more examples of it:

She is drinking some tea.

We were waiting for two hours.

Apart from using it to form the progressive tenses, the present participle can also be used as an adjective:

She called from the neighboring house .

Many learners of English mistake gerunds for present participles because they look exactly the same.

However, there is a great trick to always get them right.

Present participles can be adjectives while gerunds can be nouns :

The smoking lady was very rude. (Present participle)

Smoking is bad for your health. (Gerund)

The past participle ( not to be confused with the past tense ) ends with – ed or can be an irregular verb.

We already know it is used together with to have to form the perfect tenses:

I have broken my leg.

She will have written it by tomorrow.

Apart from this, we can also use the past participle as an adjective and to form the passive voice:

I love cooked meat. (Used as an adjective)

The house was built . (Passive voice)

The emphatic form is when the verb “to do” is added to present or past tenses to emphasize (stress) a statement or make a correction.

They can also be used in questions .

Present Emphatic

The present emphatic is formed with the simple present of the verb “to do” followed by the main verb in the simple present. 

For example:

I do speak English . 

The present emphatic can be used to ask a question. When a question is asked, the subject (the person or thing acting) and the verb “to do” are often inverted (switched).

You can use the emphatic to ask a question in the negative.

Don’t you speak English? Yes, I do speak English.

Do you speak Russian? No, I don’t speak Russian.

Past Emphatic

To form the past emphatic, you take the simple past of the verb “to do” and put it in front of the simple past.

Didn’t  you speak English when you were in Ireland?  

Yes, I  did speak English. I spoke it the whole time I was there.

There are several reasons why you should know how to use the English tenses.

English is a chronological language.

English needs all its tenses in order to locate an action in time or express the relationship between two different actions.

She arrived yesterday. (The action happened in the past.)

She will arrive tomorrow. (The action will happen in the future.)

I was having a bath when the telephone rang . (There is a relationship between the two actions. Me having a bath was an action going on for some time in the past. The telephone ringing interrupted my bath.)

Tenses can help you say what you actually want to say.

Each tense in English is used for one or more specific purposes. You will need to use one tense or another depending on what you want to say:

I drink water every day. (Repeated action.)

I am drinking water at the moment. (Action happening at the moment of speaking.)

I will drink water later. (Action in the future.)

Yesterday I drank a lot of water. (Action in the past.)

English is a world language.

English has become the main language of communication in the world.

Millions and millions of people use it every day to communicate with friends, colleagues, bosses and more.

If you want to be part of that world conversation, you should know English, and knowing English means knowing the tenses in English!

Tenses will help you get fluent.

Tenses will make the difference between being a beginner who can talk about their daily routine and speaking fluently about politics.

The more tenses you learn, the better you will get at speaking English properly.

A big difference between a beginner and an advanced learner is how well they know the tenses and how they use them.

Learning tenses can improve all your language skills.

Grammar (and tenses) are present in everything we do in English, from telling the time to wishing a happy birthday to someone.

We use tenses when we speak, listen to a podcast, watch a movie, read a book and more. So the more we know about tenses, the best we can apply our knowledge to all the other language skills.

Without tenses, passing your English exams would be impossible.

Finally, you need to learn the tenses if you want to pass your classes or ace (get high marks in) your official exams .

Even if you are learning English on your own and you don’t plan on taking an exam, you must have a reason for learning, and because of that, you will want to know if you are making progress (getting better).

Learning tenses can be quite boring sometimes, especially if you are trying to learn by yourself .

However, I have found there are some tips that are better than others when you want to learn English grammar tenses .

Divide them into past, present and future tenses.

You can use the division past, present and future to have three groups of four tenses.

This division is great if you want to learn everything about a specific “chunk” (piece) of the line of time without worrying too much about the rest, so it is a good technique for beginner learners who are still not ready to mix points in time.

For example, first you can focus on the present tenses, then the past tenses and then the future tenses.

Divide them into pairs.

Another way of dividing the different tenses is by making pairs. As you learn more, you will realize that very often tenses share certain features or like to appear together in sentences.

There are different ways of making this division:

• Simple and continuous tenses: present simple and present continuous, past simple and past continuous, future simple and future continuous.
• Perfect and perfect continuous tenses: present perfect and present perfect continuous, past perfect and past perfect continuous, future perfect and future perfect continuous.
• Tenses that normally appear together in sentences: present simple and present perfect, present simple and present continuous, past simple and present perfect , past simple and past continuous, past simple and past perfect.

You can divide the tenses in a different way if you feel two (or more) tenses go together. I recommend this technique for intermediate and advanced learners because tenses get mixed and used together.

Practice with online exercises and apps.

If you prefer learning on the go, you can also download one or more apps and use them to become a master of the language .

Here are a couple of resources that can help you practice and remember all those forms.

• The WordReference English Conjugator .  This tool will show you what pretty much any English verb looks like in any tense. Just type the verb you’re looking for into the box at the top of the page.
• englisch-hilfen.de Grammar Exercises .  If you scroll down on this page, you’ll find all kinds of exercises to help you practice different English tenses. If you like, you can do the exercises in order, but you don’t have to. You may just want to look for exercises that cover tenses you find difficult.

Create your own sentences.

I recommend you choose one tense at a time and practice writing sentences, negative sentences and questions with it.

Write as many examples as you can think of, focusing on one tense at a time. This will help you memorize the structure of the tense in all its forms and allow you to use it more naturally.

Watch series, movies and videos to see tenses in context.

Context is everything, and the more context we have, the better we understand what is being said.

The best thing about any type of native English video is that you are not only listening to native speakers talk (so you are practicing pronunciation) but also listening to full sentences in context .

When you listen to a tense being used in context, you will be able to understand why it is being used, so you will know when to use it by yourself.

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Create flashcards for each tense and how they are used.

You can do this in two separate decks (one for the uses of each tense and one for the way they are built), or put all the information together on both sides of a flashcard.

Do whatever feels best for you. The most important thing is that you keep on practicing.

Create your own short stories.

This kind of exercise is recommended for intermediate and advanced learners.

Think of a story and try to write three different versions of it, one in the past, one in the present and one in the future. At the beginning, this can feel a little bit difficult, but it will get easier as you go, I promise.

You can write about anything you want, but I like to propose the following topics to my students:

• Daily routines (Describe yesterday, a normal day in your life and tomorrow.)
• Your lives (Who were you in a past life? Who are you know? Who do you want to be in the future?)
• Cities (Write about how they were in the past, how they are now and how you think they will be in 100 years.)
• Favorite singer/writer/actor (Choose one who is already dead and one who is still alive. For the future tense, write about your favorite alive artist in 10 years. Where will they be? What will they do? Will they be married? Will they have kids?)
• Think about an important historical event and write about it by using past tenses. Now imagine the same event is happening now. Change your story accordingly.

Read, read, read!

Reading is one of the best exercises we can do if we really want to understand tenses and learn how to use them properly.

You will not think you are learning anything while you are reading, but the truth is that your brain is learning how to recognize different tenses and language structures that get repeated, and before you know it, you will start using English tenses like a boss.

Listen to podcasts and the radio.

Listening to English spoken by native speakers is also a superb way of practicing tenses.

When you listen to proper English, much as when you read it, you are feeding your brain with correct structures. With time, your brain will recognize these structures and give you a hand when you want to say something in English.

You will eventually have to know all 12 English tenses, but you don’t need to learn them all from the start.

I have divided the 12 tenses into three levels (beginners, intermediate and advanced learners). Bear in mind this is my personal opinion based on all my years of teaching English. Some courses and official exam institutions can divide the tenses differently.

What’s important here is that your knowledge of tenses goes hand in hand with the rest of your English skills. You should never know everything about, say, English vocabulary, but have only a vague (unclear, not precise) idea about basic English grammar .

Beginner tenses

The beginner tenses are those that allow us to start expressing ourselves in English with basic grammar patterns and basic vocabulary. 

These tenses are used mainly to describe the world around us, talk about what we did, do and will do, and they help us make sense of simple actions and events.

The five tenses every beginner should know are (in this specific order, ideally):

• the present simple
• the present continuous
• the future simple
• the past simple
• the past continuous

Intermediate tenses

The intermediate tenses go one step further and will be of help when you want to talk about more specific things.

They are not difficult tenses to master, but they can make the difference between a beginner grammar newbie (beginner) and a serious intermediate grammar  learner.

The tenses I believe every intermediate learner of English should know are:

• the present perfect
• the past perfect
• the future continuous

Advanced tenses

An advanced user of any language is supposed to be able to talk about any topic without hesitation (doubt), write well and master tricky advanced grammar topics .

The tenses normally reserved for advanced learners are:

• the present perfect continuous
• the past perfect continuous
• the future perfect
• the future perfect continuous

There are several great options to practice tenses. It all depends on the kind of student you are and the way of learning you prefer.

Grammar and Exercise Books

For starters, you have the traditional grammar exercise books. They are possibly the best option for learners who want to practice tenses and writing at the same time.

Many English grammar books have an accompanying exercise book. Just pick (choose) the one you feel comfortable working with.

Grammar Practice Online

There are also hours upon hours of English grammar exercises online .

If you search for [ name of the tense] + exercises , you will get many results.

The trick here is to choose the kind of exercise you like the most. Learning English grammar has to be enjoyable, so if you decide that learning tenses with fill-in-the-blank exercises is the best way for you, go for it.

Another great way of practicing tenses is by watching videos. Streaming platforms such as HBO, Netflix and YouTube are learners’ favorites when it comes to consuming video content.

Plus, they are awesome if you want to practice tenses in context and see how native speakers use them.

Undoubtedly, learning a language from its native speakers is one of the best ways to get a perfect accent, but aside from that, you can be sure the input (information) you will be receiving is 100% accurate and matches its context perfectly.

Apart from FluentU, there are many other apps for learning English grammar you can download on your phone.

Apps for learning English can be a great learning asset when they are used regularly, so remember to get your “daily portion” of English tenses no matter what.

There are hundreds of thousands of apps available.

Because of this, apps for learning and practicing tenses in English (or any other topic you may be interested in) seem to be a must-have (essential) nowadays.

How can you use apps to learn tenses in English?

So, how can apps help you while you are trying to learn English tenses?

There are many different things you can do to learn tenses in English specifically. These are some of them:

• Choose the types of exercises that focus on practicing English tenses. If your focus at the moment is on mastering the tenses in English, choose exercises that will allow you to do that. Depending on the app, you will find different types of exercises, like fill-in-the-blank, finish the sentence, choose the correct answer and more.
• Analyze dialogues/transcripts and understand why a tense was used. This is another awesome way of learning tenses. Almost every English sentence includes at least one of the 12 tenses. Analyze what you read and try to explain why a specific tense was used.
• Take online tests and quizzes on tenses to find out how you are doing. If your app has the option of taking quizzes, this will be a great way to check your progress and see which tenses you have to review.
• Listen to dialogues/podcasts/audio to hear native speakers use different tenses. You know already that the best way to learn English is by listening to native speakers talk. Just as you did with the transcripts before, write down and analyze the dialogues you listen to and try to explain why the speakers used a specific tense.
• Easily go back to any tenses that may be difficult for you and review them on the go. One of the most useful reasons for downloading an app to your phone is that it will always be with you, so you can review those harder tenses whenever you have some time, no matter where you are.

Tenses are the skeleton of a language. Without them, the majority of languages in the world would be lost in time.

We need tenses in English to locate events and get to know what happened and when. This is super important, even at the beginning of your learning journey.

So the earlier you start mastering the tenses in English, the better.

Stay curious, my friends, and as always, happy learning!

If you like learning English through movies and online media, you should also check out FluentU. FluentU lets you learn English from popular talk shows, catchy music videos and funny commercials , as you can see here:

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The FluentU app and website makes it really easy to watch English videos. There are captions that are interactive. That means you can tap on any word to see an image, definition, and useful examples.

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Learn How You Can Maintain Good Tense Control In Your Writing

What is tense control in writing?

It is staying in your selected primary tense and time and then using other tenses to highlight changes in the time frame.

Can you switch tenses in writing?

Yes, of course, you can change tenses and time. But only when you need to indicate a distinct change.

Article Contents

Time and tenses in writing

You can write in past, present, or future time in any form of writing.

In each time setting, you have a choice of verb tenses you can use.

For example, if you write in the present, you will mostly use the present simple, present continuous, and perhaps present perfect tense.

Writing in the past uses past simple, past continuous, and the past perfect form.

When writing in the future , you use will, going to, or any other future form.

Switching between past and present tense in writing is always necessary for variety.

But you need to be careful with changes in time to maintain tense control in writing.

Your choice of time and tense depends on what you intend to write.

It will be different for an essay, an article, a story, a research paper, a literary work, or a business proposal.

But you need to keep your tenses consistent.

Tenses for writing projects

Story writing tenses

For short stories and novels, the past is the most common choice.

In fact, the simple past tense is often called the storytelling tense.

You are recounting a series of events in the past through your narration and dialogue .

You indicate changes or switches in the time order of events by using good tense control and selection.

For example, the past perfect says that it is an event older than an event in the past simple.

You can use it for flashbacks to what your characters might have gone through and how they felt at an earlier time.

The past continuous is another option to indicate an action that happened at the time of a past event.

Story writing is usually written in these forms of past tense.

Switching from past to present tense in a story is possible, however.

But it is not an easy feat.

There are possibilities when the narrator moves into the present to talk about facts or generalities.

But generally, you would write most stories in the past in narrative tenses.

One other choice you need to make is your point of view when you write any story.

Essay writing tense

The general rule is to use the present simple tense when writing an essay.

You would only use past tenses if you needed to write a narrative essay, which is quite rare.

In an essay, you are usually expressing facts, your understanding or beliefs, or your opinions.

You can only express these aspects by using present tenses.

The one possibility to change tense in essay writing is when you express a possible future outcome related to your facts or opinions.

Here is a quick example.

In the book, the author says that our waterways are highly polluted and that it now affects our domestic water supply.

I believe this will become a significant problem for our children in the future.

Blog and article writing

When you write an online article or blog post, you are often talking about something that has happened. Blog writing is frequently like news reporting.

You are telling the reader about events that occurred before you sat down to write.

In this case, you would always use past tenses.

However, if you are writing evergreen content like how-to articles or advice posts, you will probably choose to use the present tense.

Anytime that you are giving advice or instructions, you use the present simple or even the imperative.

Think here about a recipe article. You would always write it in the present.

You should always be clear about your tense control in writing before you start and avoid changing tenses unnecessarily in your writing.

One last factor to consider is your choice of point of view when writing blog posts and articles.

Professional and academic writing

You can usually classify this type of writing into three main categories. You are going to write either a plan, a report, or a statement.

For a plan, such as a business plan or a proposal for a new curriculum, you will use future tenses and forms.

If you need to write a sales report or the results of a survey, you should only use the past tense forms.

A statement like a code of ethics, a statement of purpose, or a press release are most often in the present.

For these three writing tasks, when you start writing in a particular tense, you should be consistent unless there is a compelling reason to switch between tenses.

Common mistakes in changing tenses in writing

The most common error in tense control in writing is switching from past to present tense at a sentence level.

One of my favorite examples of mixing tenses in a sentence is this one.

Who said writing is easy?

It is incorrect because the first verb is past, and the second is present.

There are two ways to correct this sentence with the right tense agreement.

Who says writing is easy?

Who said writing was easy?

Both sentences now agree.

Here are a few more examples of incorrect tense agreements and how to keep tenses located in the same time.

I was walking down the stairs, and there he stands , staring at me. Incorrect

I was walking down the stairs, and there he stood , staring at me. Correct

I saw Mary this morning, and she says that she is moving to Canada. Incorrect

I saw Mary this morning, and she said that she was moving to Canada. Correct

After I booked my flight to London, I am finding that all the prices for tickets were falling. Incorrect

After I booked my flight to London, I found that all the prices for tickets were falling. Correct

Tense change in a paragraph

Changing the tense in a paragraph is also an area that can cause problems.

It often happens when you use a pronoun linking back to the previous sentence.

You need to be careful with this and that . This is for present and future references. That is for a past reference.

The police believe the suspect is armed and is suffering from a severe mental disorder. That was why everyone in the district should be on alert. The detective in charge said all precautions were being taken. Incorrect

The police believe the suspect is armed and is suffering from a severe mental disorder. This is why everyone in the district should be on alert. The detective in charge says all precautions are being taken. Correct

With future tenses, mistakes often occur in first and second conditional sentences.

You can only use the future with the present and not with the past.

If I win the lottery, I’d buy a Ferrara. Incorrect

If I win the lottery, I’ll buy a Ferrara. Correct

If I won the lottery, I’ll buy a Ferrari. Incorrect

If I won the lottery, I’d buy a Ferrari. Correct

Use natural tenses

You can write more naturally if you first work out what tenses you would generally use when you speak.

You’ll save a lot of time and spend less time editing and revising.

If you write in perfect tenses that you’re not familiar with, such as present perfect or future perfect, you might find yourself shifting back to a more natural tense.

You need to stay in your comfort zone and your natural variety of English with tenses.

For US writers, past simple is much more common than present perfect. However, for UK writers, the opposite is true.

You should stay within your local voice but still pay attention to any mixed tense mistakes.

Get some help

It’s so easy to mix your tenses. Every writer is guilty of it occasionally.

If you’re not sure, get some help. Find a friend or family member who can read your text and check.

You don’t need to hire an editor, but a fresh pair of eyes always helps.

But don’t rely too much on online grammar checkers.

They are great for finding basic grammar errors, but for tense shifts, they are not as reliable.

You can write in three different times, present, past, and future.

The English tense system has four tenses, or verb forms, for each time.

They are simple, continuous, perfect simple, or perfect continuous.

As long as you use the correct tenses for your time setting, you won’t make a mistake.

When you are writing a story, stick with the past tenses.

For an essay or advice article , stay with present tenses.

If you want to write about a plan, use the future.

It’s that easy to maintain tense control in writing.

Related reading: What Is The Subjunctive Mood And How To Use It?

About The Author

Derek Haines

More articles.

How To Write A Blog Post To Attract Attention And More Readers

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7 thoughts on “learn how you can maintain good tense control in your writing”.

Great article! I did notice one potential error in your favourite sentence though… maybe you’ll like it even better with this alternative interpretation ;)

I would argue the primary issue with this sentence is not that it’s changing tense in the middle, but that it’s lost its quotation marks.

I’ve noticed this sometimes happens with well-known rhetorical questions.

Put them back, and there’s no problem:

Who said “writing is easy”?

Now the sentence is no more incorrect than any other dialogue. The narration is in the past, but the speaker is using the present tense. As you pointed out, you can always have narration and dialogue differ.

Laura said “writing is easy.” Laura said “I like peas”. Jill will respond, as she always does “But you used to hate peas, and writing has not always come naturally”.

It’s an example phrase, so I didn’t think it needed to be written in strict dialogue. But yes, it could also be written in different ways. “Who said writing is easy?”, I asked. “Who said writing is easy?”, I wondered. But thanks for your interest and interaction.

Hi Lisa, I have been reading (and rereading) your articles on writing. I have found them to be very interesting and very inspiring. I am enquiring if I can download all your articles into a Word document, so I can read the offline. I know I could this without your knowledge but I find it polite to ask permission to do so.

That’s fine with us, Paul. Glad to hear that our writing articles are useful for you.

I’m from Philippines, greetings from filipinos! Such a good starter pack about writing. It’s very useful, keep it up! :)

Hi Lisa, great article, as all that you write. Keep on doing the good work. And keep on smiling, too.

Hi Lisa- Thanks for the heads-up to writers about the need that “other set of eyes”, professional eyes of an editor. It seems we have a bad rep from some writers, but all we really want to do is help; help the writer with clarity and simple corrections. All this so that the reader is not confused or distracted.

Comments are closed.

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Using the Present Tense with Works; or, Othello Still Exists

It is customary to use the present tense when discussing a literary work:

Othello is a play by Shakespeare. It begins on a street in Venice, where Roderigo and Iago are arguing .

Some of the themes of Othello are racism, love, jealousy, and betrayal.

Like other Shakespearean tragedies, Othello has five acts.

The play ends with Othello’s murder of Desdemona and with the revelation of Iago’s motives.

Likewise, use the present tense to describe the actions of characters and the movement of plot:

In act 3 Iago persuades Othello that there is reason to doubt Desdemona’s faithfulness, and in the final act Othello confronts Desdemona and then strangles her to death.

The rationale for using the present tense when discussing a work is that the work exists in the present just as it existed earlier: Othello always has five acts and always ends with the same actions.

The principle applies to works of all sorts, from literary criticism to films to websites:

William Hazlitt’s Characters of Shakespeare’s Plays contrasts expressions of passion and violence in Othello and Macbeth .

A 1965 film version of Othello stars Laurence Olivier.

The blog Bard Film analyzes references to Shakespeare in popular films.

By extension, you may also use the present tense when the subject is not the work itself but the work’s author, if the work is implied. In the following examples, “in Othello ” is implied:

[In Othello ] Shakespeare gives Iago, an archetypal villain, an important role.

By identifying Othello as a Moor, Shakespeare introduces both racial and religious issues [in Othello ].

Acceptable Uses of the Past Tense

If you’re primarily discussing the historical context of a work, however, use the past tense:

By identifying Othello as a Moor, Shakespeare introduced both racial and religious issues to early modern playgoers.

Use care when choosing between the past and present tense. A good rule of thumb is to consider whether the principal context of your discussion is textual or historical.

When the context is clearly historical, the choice of the past tense is obvious:

Othello was first performed in 1604, at Whitehall Palace in London.

Shakespeare composed Othello about fifteen years after Marlowe wrote Tamberlaine .

Aim for Consistency

Above all, aim for consistency and try to avoid frequent shifts in tense, which can be jarring for readers. It’s easy to shift tenses without realizing it. Here’s an example that uses tenses inconsistently:

For the plot of Othello , Shakespeare adapts a sixteenth-century Italian tale, while Christopher Marlowe based his play Tamberlaine on the life of an Asian emperor.

In this sentence about the sources of Elizabethan dramatists it would be better to keep the verb tenses consistent and use adapted and based .

Here’s another example of tense shifting:

The details of Othello’s narrative come from medieval and early modern travel books, some of which described fantastic creatures.

The author uses the present tense for the main text under discussion, but for the other texts—the travel books—switches to the past tense. The switch is understandable: the travel books inspired Othello’s narrative in the past, when the narrative was created. Using the present tense consistently, however, would accurately reflect the status of all the texts mentioned in the sentence as works that exist in the present: The details of Othello’s narrative come from medieval and early modern travel books, some of which describe fantastic creatures.

A word of caution for copyeditors: if an author uses the past or present tense in a consistent manner when discussing works, pause before you follow an impulse to change the tenses, especially if such an intervention would be extensive. The author may have sound reasons for his or her choices, and you would do better to query before you impose one tense over another. If you encounter frequent shifting of tenses for no discernible reason, revising for consistency is a good idea.

marian 06 October 2019 AT 07:10 PM

When quoting literature that is originally written in the past tense, which tense should be used to introduce the text - present or past?

Example for essay on Edith Wharton's Ethan Frome: When Ethan and Mattie return from town, they, “passed into the kitchen, which had the deadly chill of a vault after the dry cold of the night."

Writing an academic essay for a Literature course, I should write in the present tense and I should be consistent with tense. Which rule applies here?

Your e-mail address will not be published

Erika Suffern 07 October 2019 AT 08:10 AM

The way you have written it is correct. Don't change the verb tenses in quoted material.

Veronica M. Schuder 12 December 2020 AT 04:12 PM

The second use of this word, which occurs when Mrs Mallard sits down and “abandoned herself” (756), offers an alternative meaning.

That sounds wrong. Can I change the tense to present with brackets: “abandon[s] herself” (756). ?????

Erika Suffern 14 December 2020 AT 09:12 AM

The short answer is yes. For additional guidance, please see our post on changing verb tenses in quotations: https://style.mla.org/using-brackets-to-change-tense/

Professor Rosalind Horowitz 10 March 2021 AT 12:03 PM

In the study of ancient Judaic literature, for example, Rashi, a brilliant commentator of texts, we always use the present tense to show that the scholarship is not archaic but still relevant and useful to current life. It is a way of putting the text inside the contemporary world.

Sara 16 June 2022 AT 05:06 PM

When writing about a text in MLA should you write in simple present or present continuous? or does it not matter?

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Shifty Tenses

Many students believe that tense changes should always be avoided. However, sometimes a shift in tense is necessary to indicate a change in the timeframe of the action. It is the unnecessary shifts in tense that sometimes cause awkwardness and should be avoided.

What is tense?

Tense is the grammatical word to describe the ending of a verb (usually -ed for past and -s for present). English usually marks the sense of time with an adverb (for example: it is happening today or it happened yesterday.) When proofreading for unnecessary tense shifts, there are several questions to keep in mind: “When do I want to set this action?” and “Has the time period changed?” For example, I may be writing an essay about my experiences on a recent trip to Virginia and want to say that I saw the Mason-Dixon line for the first time. I have several options. I can write my paper in the past tense, which is the style most people are used to reading in novels or short stories, as follows:

Then the driver pointed to a white line painted on the road and said , “There's the Mason-Dixon line.”

However, I may choose to make my essay more immediate by placing the action in the present. This is also an acceptable writing style, especially for an essay:

Then the driver points to a white line painted on the road and says , “There's the Mason-Dixon line.”

In this case, the tense is merely a matter of style; it is your choice.

Should I ever change tense?

Sometimes it is necessary to change tense. For example, if the time frame of the action changes from past to present, the tense should change to indicate this:

Although it was only a four-hour ride from my home in Pennsylvania to my boyfriend's home in Virginia, I was terrified. Looking back, I think my feelings may have been influenced by stereotypes of the Old South.

Although this paragraph starts in the past tense, the phrase “Looking back” clearly shows the time frame of the action “think.” The tense change is perfectly acceptable without this phrase also:

Although it was only a four-hour ride from my home in Pennsylvania to my boyfriend's home in Virginia, I was terrified. I think my feelings may have been influenced by stereotypes of the Old South.

The reason for this tense change is that I am thinking now—in the present time. Notice how putting that sentence in the past tense changes the time frame and ameaning of the action.

I thought my feelings may have been influenced by stereotypes of the Old South.

Now it sounds as though I was reflecting during the car ride, but I wanted to imply that it was only later that I had this thought.

When is it wrong to shift tense?

There are other times, though, when a tense shift is not correct. For example, if the action all happened in the same time—past, present, or future—then the verbs should be consistent in tense. This “mistake” is often heard in speech, and it is even used in very informal writing. However, from a grammatical viewpoint, this type of unnecessary shift in tense should be avoided in more formal (such as academic) writing.

I climbed out of the car, walked through the door, and prepared to meet “the parents,” but instead a large, honey-colored dog runs to meet me at the door.

Here is a better way of writing this sentence:

I climbed out of the car, walked through the door, and prepared to meet “the parents,” but instead a large, honey-colored dog ran to meet me at the door.

What tense is best for my paper?

There are other uses of tense that a college student should be aware of. English majors and others who write analysis papers will often write in the literary present. This allows a writer to write about fictional or nonfictional information from a literary work in the present tense.

Shakespeare uses many risqué puns in his plays. In one scene, he disguises sexual references as sword-fighting terms.

Although Shakespeare wrote many years ago, his work can still be talked about in the present because it still exists. Again, though, there is no hard and fast rule about tense. If you wanted to stress that this happened in the past, perhaps as part of a biography of Shakespeare which was describing how he used to write, you may choose to write it in past tense instead of the literary present:

Shakespeare used many risqué puns in his plays. In one scene, he disguised sexual references as sword-fighting terms.

This is also grammatically correct, but it changes the focus of the sentence slightly. Now it reads more like a narrative than an analysis. In a situation like this, when both styles are equally grammatically correct, it becomes a choice of deciding which is better for your purposes. Are you analyzing or narrating?

I'm still confused. Do you have an example of a paper that uses tense changes well?

The following is an example of an essay that uses tense changes successfully. Read the description of how the student changes tense and then pay attention to the effects of the tense changes as you read his essay. Some of the verbs in the essay are in bold to help you spot these changes. (This example is taken from a paper by a former student in Professor Blackledge's Theater Criticism class.)

This writer begins in past tense to talk about a specific production of the play. Then he shifts into something called the universal present to make the reader feel like an observer watching the play unfold. Then he shifts back to past tense when making a comment about the play—something he felt when he was watching it. Then he shifts again to present tense in the final paragraph to state his overall evaluation of the play.

The Pittsburgh Public Theater production of The Pirates of Penzance presented their audience with a lighthearted theatrical experience that could be enjoyed by theater goers, young and old alike. Written over one hundred years ago by the legendary operetta team Gilbert and Sullivan, Pirates has enjoyed great success with each new incarnation. Pittsburgh Public managed to hold to this tradition once again by bringing this ageless piece of musical theater to life with freshness as well as a salute back to the days of theater past. This look back to the theater of Victorian England was most evident in the set design of Michael Schweikardt. When the audience first enters the theater their eyes are drawn to a large, false proscenium painted bright red to resemble a large red curtain found in an old opera house. At the top of the proscenium are the stylized letters “G&S” to acknowledge the plays creators. Hanging from the ceiling are two gaslight chandeliers helping to transport the viewers back in time to the world of Gilbert and Sullivan. The playing area in front of the proscenium is painted in a fashion which resembles the lobby of a grand opera house. Disrupting this grand Victorian vision are two brown boulders placed on either side of the stage, foreshadowing the scene that is to be staged. These aspects create a mood for the audience by acknowledging them and allowing them to just enjoy the show. As the curtain rises , the audience views the backdrop depicting a rocky beach with a ship anchored in the distance. Set in front of the backdrop is a rocky hill indicating that the action will take place on a flat of land above the beach. Cut into the hill is a tiny cave which will serve as a hiding place later in the act. The actors' entrances are made coming over this rocky structure and entering onto the stage. I felt that Michael Schweikardt's design was very effective in meeting the needs of this scene. We must take into account that most of the action takes place in front of this set and allows the twenty-member cast to move freely within their world. Once again, the image of a late nineteenth-century production is perceived with the use of the simplistic structures and the simply painted backdrop. I feel that Mr. Schweikardt's challenge in designing this production would be to provide a functional set without taking away from the spirit of this timeless tale. The Pirates of Penzance was written for a different audience from today's. In a time when landing helicopters on stage and giant chandeliers come crashing down dramatic finales, audiences expect more. By focusing the spectacle of his set to bring the audience into the time this operetta was first produced, the audience is satisfied from the beginning. They can better understand why there is still a demand for century-old productions like The Pirates of Penzance and enjoy the experience today.

by Jessica Knouse

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• Published: 03 June 2024

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

• Wenchao Li 1 &
• Haitao Liu 2  

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

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

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Scaling neural machine translation to 200 languages

Conventional machine learning technology in aes.

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

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

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

Deep learning technology in AES

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

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

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

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

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

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

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

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

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

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

Inflectional

give, give-s, gave, given, giving

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

足-が 棒-に なり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

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

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

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

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

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

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

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

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

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

The research questions addressed in this study are as follows:

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

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

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

Methodology

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

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

Measures of writing proficiency for nonnative Japanese

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

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Criteria (output indicator)

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

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

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

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

Moving average type-token ratio.

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

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

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

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

Number of errors per essay.

Japanese essay text

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

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

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

Statistical analysis

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

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

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

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

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

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

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

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

Step 1: Construct a weight matrix W as follows:

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

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

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

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

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

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

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

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

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

Results and discussion

Annotation reliability of the llm.

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

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

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

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

Inter-annotator agreement (human–human annotator agreement)

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

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

Agreement of annotation outcomes between human and LLM

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

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

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

Reliability of LLM-driven AES scoring

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

Reliability of LLM-driven AES discriminating proficiency levels

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

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

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

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

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

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

Prompt strategy

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

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

PRMSE scores of the 18 AES models.

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

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

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

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

Data availability

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

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

SPOT is a specific component of the TTBJ test.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Article   Google Scholar  

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

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

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

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

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

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

Google Scholar  

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

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

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

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

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

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

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

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

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

Article   CAS   PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

Article   PubMed   Google Scholar  

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

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

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

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

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

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

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

Article   MathSciNet   Google Scholar  

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

PubMed   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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.

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

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

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Maya Hawke Doesn’t Want to Let the Vibe Die

She just finished playing Flannery O’Connor and released a new album. Next up: “Inside Out 2” and a new season of “Stranger Things.”

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By Leigh-Ann Jackson

The actress and singer-songwriter Maya Hawke is on a self-awareness kick.

Lately, she has been mindful of how she communicates, making an effort to be sincere and open. Career wise, she said, she’s been striving to foster collaboration and “no-bad-ideas energy.”

Hawke, 25, plays Flannery O’Connor in “Wildcat,” a film released last month that is co-written and directed by her father, Ethan Hawke. In 2023, she acted alongside her mother Uma Thurman in the cinematic caper “The Kill Room.”

Hawke has also been checking in with herself, making body scans part of her bedtime ritual. “It’s like a meditation thing where you tense all the different muscles in your body individually and try to let out your feelings,” she said during a video interview.

Stress abatement is crucial as she focuses on back-to-back projects. She’s filming the final season of “Stranger Things” in Atlanta, her third album, “ Chaos Angel, ” dropped on May 31, and Pixar’s “Inside Out 2” — she voices a new character, Anxiety — opens June 14.

Hawke got introspective as she talked about being “a little bit of a hypochondriac,” her habit of losing things and her love of a loose fit. These are edited excerpts from the conversation.

Voice Memos

I’m a person who’s pretty constantly creative. That doesn’t mean all of the things I create are good. They’re mostly bad. But I try to keep track of them because I never know the difference in the moment. I always try to take voice memos of little melodies that I’ll come up with or an idea for a scene I’m writing or something I think my character should say that came to me randomly.

One-Gallon Water Bottles

When things are big, I lose them less, and I like not having to refill so many times. I was working days on a movie and at night on “Stranger Things,” sleeping two hours a night. I did research into how to make yourself function on very little sleep; you’ve got to eat a lot of food and drink a lot of water.

When you’re doing a scene, you can’t deny your scene partner’s choice. That’s a pet peeve of mine — people who shut down ideas before they’ve been incubated. It’s really important to take what other people give you and go, “OK, great. You’ve put this on the table, now let me talk to you about what I want to do with it.”

My favorites are AirPods, but I like the giant ones because I don’t lose them as much. The little ones, they’re better for running and walking around. For flights, I like the big ones for falling asleep — I love the sound cancellation. It turns the airport into my little happy place.

Charging Cables

I kind of hate technology, but I can’t lie about how much I use it — all the time. There are these essential things you need to bring with you, like power cords. I get very frustrated when I’m out in the world and don’t have my charger, and I’m like, “Oh no, I’ve got my headphones in and I’ve got my little vibe cooking, but my vibe is going to die any minute now!”

Art Supplies

I have a particular addiction to a watercolor and gouache brand called Kissoh. The colors are so bright, and you can manipulate them with water and layer them really nicely. In my line of work, there’s a lot of downtime. There’s nothing better than visual arts to inspire your brain and keep you occupied.

Confidentiality

I really want to be someone people can trust with their stories. Sometimes, you’re in a situation where you’re like, “Oh, no one would ever know if I told this story.” I’ve been working on it a lot.

Band-Aids, Neosporin, hydrogen peroxide and some Advil, especially when you travel. You never want to be in a foreign country and not have your fluconazole if you need it. I do get in my head when I feel unwell and I can get paranoid, so I like to have stuff with me to make myself feel like something’s not going to go horribly wrong. I’m sick right now, and it makes me feel better that I have my liposomal vitamin C and little weird tinctures to feel like I’m participating in my healing process.

Keeping Things Honest

I try not to have any secrets. Like, I don’t want to have it be a secret when I’m really mad at my brother. I’m just going to call and tell him I’m mad, and we’re going to try to work it through.

Wizard Clothes

I like clothes that are comfortable. I don’t want elastic at the waist, I don’t want straps making red marks. An essential thing is: How can I look as dramatic as possible and feel as comfortable as possible? You’re not just in sweatpants and a baggy sweatshirt, you’re kind of witchy, dramatic and fabulous. Nice flowy pants, a loosefitting black dress, and then a big black coat.