output hypothesis writing

• Corpus ID: 142324244

Output Hypothesis： Its History and Its Future

• Merrill , Swain , +1 author Luxin
• Published 2008
• Linguistics, Education
• Foreign language teaching and research

43 Citations

The output hypothesis and its implications for language teaching, a review of the application and development of output hypothesis theory in foreign language teaching, implication of output hypothesis on teaching college english writing, a review of output hypothesis in college english teaching and learning, discussion: multiple approaches in clil: cognitive, affective and linguistic insights, foreign language activities on times: a sampler of ideas from the spanish and korean modules, role of language exposure in learning l2: persian learners of english in english vs. non-english speaking environments.

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The Effect of Comprehensible Input and Comprehensible Output on the Accuracy and Complexity of Iranian EFL Learners’ Oral Speech

Pragmatic competence in business context: a case study of thai efl university students, effects of embedded summary writing on efl learners’ anxiety and oral production in a computer-based testing environment, related papers.

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The Output Hypothesis: Theory and Research

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In the 1980s, the word “output” was used to indicate the outcome, or product, of the language acquisitiondevice.Outputwas synonymouswith “what the learner/system has learned.” In the decades that have followed, therefore, it is perhaps not surprising that the second language learning literature has been slow to take on the concept of output as part of the process of learning, not simply the product of it. In this chapter, my intent is to provide a description of the “the output hypothesis”: the context in which itwasproposed, supporting researchand theoretical underpinnings, and future directions. This review will show that though an uphill battle, there has been a shift in meaning from output as a noun, a thing, or a product to output as a verb, an action, or a process.

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• > Journals
• > Language Teaching
• > Volume 52 Issue 1
• > Collaborative writing

Article contents

Collaborative writing.

Published online by Cambridge University Press:  21 December 2018

Writing has generally been perceived as a solitary activity, completed by the writer working alone. Yet, over the years we have witnessed a growing interest among researchers and educators in Collaborative Writing , an activity that can be simply defined as the involvement of two or more writers in the production of a single text. This interest has been driven by two main factors. The first factor is the nature of workplace writing. Studies (e.g. Ede & Lunsford 1990 ; Mirel & Spilka 2002 ) have shown that in a number of workplaces, writing is often completed in teams rather than individually. The second factor is the advent of Web 2.0 applications such as blogs, wikis, and Google Docs, which have transformed literacy practices, making the creation and sharing of texts easier and more readily acceptable (Hyland 2016 ; Vandergriff 2016 ). In the field of second language (L2) learning, interest in collaborative writing was also spurred by early research conducted by Swain and her colleagues (e.g. Swain & Lapkin 1995 ; Swain 1998 ; see also timeline for additional references) showing the language learning opportunities of communicative tasks which involve joint written output (e.g. Dictogloss).

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• Volume 52, Issue 1
• Neomy Storch (a1)
• DOI: https://doi.org/10.1017/S0261444818000320

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Journal of Advances in Education Research

The output hypothesis and its implications for language teaching.

DOI: 10.22606/jaer.2020.53005

• Weiwei Sun * School of Foreign Languages and Literature, Beijing Normal University, Beijing, China

[1] Ellis, R., & He, X. (1999). The roles of modified input and output in the incidental acquisition of word meanings. Studies in Second Language Acquisition, 21(2), 285-301.

[2] Fang, L. l. (2004). The application of the length approach in college English teaching. Foreign Language World, (3), 40-45.

[3] Hong, L. (2012). An experimental study of output and retelling in second language acquisition. Education Research Monthly, (10), 108-110.

[4] Izumi, S. (1999). Promoting noticing and SLA: An empirical study of the effects of output and input enhancement on ESL relativization (Unpublished Doctoral dissertation). Georgetown University, Washington, DC.

[5] Izumi, S. & M. Bigelow, M. Fujiw ara, & S. Fearnow. (1999). Testing the output hypothesis:effects of output on noticing and second lan-guage acquisition. Studies in Second Language Acquisition, 21, 421-452.

[6] Izumi, S., & Bigelow, M. (2000). Does output promote noticing and second language acquisition?. TESOL Quarterly, 34(2), 239-278.

[7] Krashen, S. (1985). The Input Hypothesis:Issues and Implications. Harlow: Longman.

[8] Krashen, S. (1998). Comprehensible output?. System, 26(2), 175-182.

[9] la Fuente, D., & José, M. (2002). Negotiation and oral acquisition of L2 vocabulary. Studies in Second Language Acquisition, 24(1), 81-112.

[10] Li, H. (2002). The cognitive foundations of the Output Hypothesis. Foreign Languages and Their Teaching, (02), 10-12.

[11] Li, P. (2006). Review of the research on the Output Hypothesis. Foreign Languages and Their Teaching, (07), 60- 64.

[12] Lv, L. S. & Cai, L. P. (2004). Two hypotheses and English writing. Journal of Guangzhou University (Social Science Edition), (07), 48-50.

[13] Ma, L. (2017). The application of recitation-input model in writing teaching practice. Language Planning, (32), 8-9.

[14] Mackey, A. (2006). Feedback, noticing and instructed second language learning. Applied Linguistics, 27(3), 405- 430.

[15] Mackey, A. (2007). Conversational interaction in second language acquisition: A series of empirical studies. Oxford, England: Oxford University Press.

[16] McDonough, K. (2005). Identifying the impact of negative feedback and learners’ responses on esl question development. Studies in Second Language Acquisition, 27(1), 79-103.

[17] Nie, Q. P. (2002). The input and output of language and foreign language teaching. Journal of Shandong Normal University (Social Science Edition), (04), 123-125.

[18] Nunan, D. (2004). Task-based language teaching. Cambridge, England: Cambridge University Press.

[19] Shehadeh, A. (2002). Comprehensible output, from occurrence to acquisition: An agenda for acquisitional research. Language Learning, 52(3), 597-647.

[20] Shen, D. & Lu, G, J. (2019). Research on the effect of "output" based on UNIPUS autonomous learning platform on students’ English vocabulary acquisition in applied undergraduate colleges. Journal of Jishou University (Social Science Edition), 40(1), 170-177.

[21] Song, X. P., & Dai, W. D. (2009). A study on input enhancement, output and noticing. Foreign Languages and Their Teaching, (10), 5-9.

[22] Swain, M.(1985). Communicative competence: some roles of comprehen-sible input and comprehensible output in its development. Rrowley, M: New bury House.

[23] Swain, M. (1993). The output hypothesis:Just speaking and writing aren’ t enough. The Canadian Modern Language Review, (50), 158-164.

[24] Swain, M. (1998). Focus on form through conscious reflection. Cambridge:Cambridge University Press.

[25] Swain, M. (2005). The output hypothesis: theory and research. Mahwah, NJ:Lawrence Erlbaum.

[26] Swain, M., & Lapkin, S. (1995). Problems in output and the cognitive processes they generate: A step towards second language learning. Applied Linguistics, 16(3), 371-391.

[27] Swain, M. (1997). The output hypothesis, focus on form and second language learning. Hong Kong: University of Hong Kong.

[28] Swain, M. (1999). Integrating language and content teaching through collaborative Tasks. In W.a.Renandya & C.S. Ward (Eds.), Language teaching: New insights for the language teacher (pp. 125-147). Singapore: Regional Language Center.

[29] Swain, M. (2001). Integrating Language and Content Teaching Through Collaborative Tasks. Canadian Modern Language Review, 58(1), 44-63.

[30] Swain, M.(2005). The output hypothesis:theory and research. Mahwah, NJ: Lawrence Erlbaum.

[31] Swain, M., & Yang, L. X. (2008). Output hypothesis: Its history and its future. Foreign Language Teaching and Research (bimonthly), 40(1), 45-54.

[32] Wang, C. M. (2005a). The Length Approach to Foreign Language Learning. Foreign Languages in China, (01), 45-49.

[33] Wang, S. D. (2001). Input and output: The analysis of non-fluency in English class conversation. Journal of Hengyang Normal University (Social Science Edition), (1), 122-124.

[34] Wang, Y. (2005). The psycholinguistics foundation of the Output Hypothesis. Foreign Language Education, (04), 18-22.

[35] You, Q. D. (1997). An analysis of “input” and “output”. Foreign Language World, (04), 50-53.

[36] You, Q. D. (2001). The emphasis of input and output in language application ability. Foreign Language World, (06), 40-43.

[37] Zhong, L. & Wang, T, F. (2008). The influence of the length approach on English writing ability factors-A research report of the length approach in English writing teaching practice. Humanities & Social Sciences Journal of Hainan University, (02), 230-236.

[38] Zhong, M. (2010). Organic combination of “input” and “output” to promote students’ effective learning. Journal of Southwest Minzu University (Humanities and Social Science), 31(1), 37-40.

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A Critique of Merrill Swain’s Output Hypothesis in Language Learning and Teaching

Merrill Swain, who coined the Output Hypothesis, has been one of the important figures in the Second Language Acquisition field. She propounded her theory as complementary to Stephen Krashen’s Input Hypothesis suggesting that learners cannot attain full grammatical competence merely through input processing in a language. The Output Hypothesis has been quite successful in terms of shedding some light on unanswered questions related to output. However, there are still some deficiencies in interpreting what the hypothesis intended to explain. Therefore, the purpose of this paper is to critically review the Output Hypothesis by concentrating on the deficiency of an operational definition of comprehensible output, scarcity of output instances and problems arising from forcing learners to produce language.

Related Papers

Victor A Birkner

This article discusses the impact that both input and output hypotheses have on second language learning. For decades, there have been opposing ideas in this regard. Several studies were consulted to have a clearer view of which approach is more recommendable for people involved in language teaching. For several years teachers of second language strongly believed that learning a new language depended on the exposition of learners to the target language. For effective learning, the exposition should be higher than the students' level but comprehensible enough for them to understand. However, this hypothesis was rejected when a new study revealed that the exposition was not enough. Students need to use the language they have learnt. These new ideas emerged from the output hypothesis that had a significant impact on the field of language teaching. The article concludes that both approaches can be used complementarily although more study is still needed.

TESOL Quarterly

Julie Whitlow

emna maazoun

This paper sheds light on the role of output in shaping input. Output as a learning factor has been neglected with input approaches of Second Language Acquisition (SLA) which minimize the role of learner output (Gass, Mackey, 2007). Currently, some authors suggest that output facilitates second language learning by providing learners with opportunities to produce 'comprehensible output, learner's output is value laden and it informs teachers about students' needs and deficiencies in specific areas. Also, it makes teachers and practitioners revise their pedagogical assumptions about teaching, testing and curriculum designing .In addition, highlighting the role of output in shaping input is better understood via teachers' corrective feedback in classroom situations. So, teachers' reactions to learner output should be goal oriented to shape a comprehensible input through enhancing interaction in the sheltered environment. To better clarify the relation between output and input we can speak about feedback to feed-forward. So, output feeds input and input leads to output. We come up with a vicious circle that deepens our understanding of teaching and learning.

Language Learning

Ali Shehadeh

Samaraat Center

Second language acquisition (SLA) has been a concern of many teachers and researchers since the early 1960s. One of the issues related to SLA has been fi nding the techniques which effectively focus the learners' attention on the target form. A number of theories and methods have been advocated for this purpose, ranging from implicit options to more explicit ones. Although each of the suggested methods has its own advantages, they have, so far, proved insuffi cient to get learners to be able to notice the gap between their own interlanguage forms and the target language forms. Swain (1995, 1998) has argued that apart from providing the learners with comprehensible input, compre-hensible output also plays a benefi cial role in L2 acquisition. By encouraging the learners to speak or write in the L2 and providing them with the opportunities to do so, the learners can notice that they are not able to say what they want to say in the target language. On the basis of Swain's output hypothesis, we assumed that encouraging adult learners (university students) to produce target-like output would promote their achievement of the grammatical competence necessary for producing academic tests. The purpose of this article is to present the evidence from a classroom based , small-scale study of the effect of output on learner acquisition of L2 modal verbs, adjectives and adverbs conveying the meanings of uncertainty, all of which are parts of speech that are important metadiscourse items. The results of the present study suggest that an approach in which students are encouraged to produce comprehensible output, combined with their being provided with learning reinforcement ensured by appropriate feedback, can be an effective source of establishing long-lasting grammatical accuracy in the students' target language.

Studies in Second Language Learning and Teaching

Alessandro Benati

In this paper, a review of the role of input, output and instruction in second language acquisition is provided. Several pedagogical interventions in grammar instruction (e.g., processing instruction, input enhancement, structured output and collaborative output tasks) are presented and their effectiveness reviewed. A final and overall evaluation is provided at the end of the paper.

Khaerudin 0406

¡¡¡¡ This paper explores the role of input and output in second language acquisition £¨SLA £© by e m ploying Lev S £® Vygotsky ¬ð s £¨1896 £-1934 £© socioculturaltheory £® Krashen ¬ð sinput hypothesis £¨1982 £¬ 1985 £© and Swain ¬ð s output hypothesis £¨1985 £¬ 1993 £© hold different and so mew hat contradictory view points on the roles of input and output in SLA £® The difference might be solved by applying Vygotsky ¬ð s sociocultural theory £¬ w hich claims hu man higher mental functioning is constructed in a social £¬ cultural £¬ historical £¬ and institutional context £® This context is a web w oven by social interactions £¬ a dialectic unity of input and output £® Therefore £¬ according to Vygotsky ¬ð s approach to the understanding of learning £¬ the interactions between input and output give rise to second language develop ment £® Key w ords sociocultural theory £» input £» output £» second language acquisition 1 £® Introduction Second language researchers have not reached an agree m ent about the roles of input and output in second language acquisition £¨SLA £© £¬ although both have been widely studied respectively and separately £¨Krashen 1989 £» Sole 1994 £» Constantino 1995 £» Krashen 1997 £» Swain £¦ Lapkin 1995 £» Izu mi et al £® 1999 £» Izu mi £¦ Bigelow 2000 £» Feng £¦ Huang 2004 £© £® Studies on the role of input m ainly used Krashen ¬ð s input hypothesis £¨1982 £¬ 1985 £© as their theoretical fra m ew orks £¬ w hereas studies on that of output typically based their research on Swain ¬ð s output hypothesis £¨1985 £¬ 1993 £© £® These tw o hypotheses £¬ however £¬ hold different view points about the roles of input and outputin SLA £® Krashen £¨1982 £¬ 1985 £© claim ed that only co m prehensible input causes language acquisition £¬ w hile Swain £¨1985 £¬ 1993 £© proposed that through output £¬ either speaking or writing £¬ language acquisition might occur too £® Their discrepant views aboutthe roles of input and output leave language teachers and learners in a dile m m a £® Should teachers m aximize co m prehensible input by providing students with lectures £¬ reading progra ms £¬ and listening opportunities £¬ as Krashen suggested £¬ or to arrange considerable speaking and writing opportunities for students to practice as Swain im plied £¿ The tim e in class or the tim e for learning a new language is limited £¬ and m ore tim e and effort on input m eans less tim e on output £® So m e researchers claim that both input and output are essentialin language learning £® However £¬ their claim cannot find support fro m relevant SLA theories £® If they cite Krashen ¬ð s £¨1982 £¬ 1985 £© point to support the m £¬ they cannot use Swain ¬ð s output hypothesis £¨1985 £¬ 1993 £© £¬ because £¬ logically £¬ one cannot believe that output does not cause acquisition and output leads to acquisition at the sa m e tim e £® This paper tentatively applies the sociocultural theory of Vygotsky £¨1896 £-1934 £© £¬ one of the m ost influential philosophers and psychologists in the 20th century and a representative of social constructivism £¬ to explore the roles of input and output in SLA £® As an inter £-discipline £¬ SLA has constantly been nourished by theories in the fields of philosophy £¬ psychology £¬ and sociology £¬ as well as linguistics £® 7 8

Mila Tasseva

Studies in Second Language Acquisition

Kara Morgan-Short

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AI Prompting for Writing: How to Get High-Quality Output

Want to improve the quality and consistency of AI’s written output? Wonder how prompts, context, and templates can help?  

In this article, we explore AI prompting techniques for getting quality written results.

Why It’s Important to Balance AI Assistance With Human Creativity and Expertise

There are currently two extreme views on artificial intelligence (AI) in content creation: those who argue against using AI at all–claiming it produces low-quality content and lacks the human touch and those who are using tools that automate the entire content creation process, which could potentially flood the internet with AI-generated content.

Looking ahead, humans will remain crucial to the content creation process, at least in the near future. The approach is to use AI as an editor or assistant rather than relying on it to create content from scratch.

This balanced approach allows content creators to leverage AI's strengths (such as rapid information processing and language generation) while maintaining the unique insights, creativity, and emotional intelligence humans bring to the process.

As AI technology improves, people may care less about whether content was created by AI or humans as long as it provides value. 

The following tips will help you deliver truly valuable content that stands out because of its strong human influence.

#1: Start With Clear, Specific AI Prompts That Include Detailed Instructions

When writing effective AI prompts , it's crucial to be very clear and specific with your instructions. If you think of AI as a super knowledgeable, almost genius intern, but one that lacks judgment and tends to be “lazy,” doing exactly what you say without questioning it, you'll be on the right track for communicating with it. You need to spell out exactly what you want.

For example, instead of just saying, “Write a blog post about my morning routine,” provide your AI writing assistant with these instructional guides:

• The goal of the content (e.g., to inform, persuade, or entertain)
• The target audience (e.g., busy entrepreneurs, college students, new parents)
• Writing guidelines (including tone, style, and any specific requirements)
• A detailed template or structure for the content
• Examples of what to avoid (e.g., clichés, overly promotional language)
• References to writing styles or publications that exemplify the desired output

For example:

Write a 1,000-word blog post about a productive morning routine for busy entrepreneurs. The tone should be motivational yet practical. Include 5 key steps, each with a brief explanation and a real-life example. Conclude with a call to action, encouraging readers to try this routine for a week. Avoid using cliché phrases or overly promotional language. The writing style should be conversational but professional, similar to articles found in Harvard Business Review .

#2: Give Your AI Writing Assistant Context

Another key tip is to provide relevant background information to the AI. This might include details about your business, target audience, or personal preferences. 

Keep these context cues relatively short—just a few lines or a paragraph—to avoid overwhelming the AI tool with too much information. 

In ChatGPT , you can save this information in the “Custom Instructions” feature, which automatically applies to every new chat. Alternatively, you can save these contextual cues in a notepad to easily add them to your AI writing prompts when needed. However, don't rely too heavily on this feature, as it can sometimes lead to repetitive or overly constrained outputs.

Curious About How to Use AI?

We recently launched a new show that help marketers, creators, and entrepreneurs understand the business applications of AI. It's hosted by Michael Stelzner and explores this exciting new frontier in easy-to-understand terms. Pull up your favorite podcast app and search for AI Explored. (Look for the cover art shown to the right.) Or click the button below for more information.

I'm a solopreneur and content creator on Twitter. My content focuses on productivity hacks and time management techniques for freelancers and small business owners. My audience consists primarily of millennials and Gen Z professionals who are looking to optimize their work processes and achieve better work-life balance. My writing style is typically informal and engaging, with a focus on actionable advice and real-world examples. I often use humor and pop culture references to make complex topics more accessible .

#3: Use Templates for Consistent Structure in Your Content

A template gives your AI assistant the structure you want it to follow, almost line by line. This helps ensure you get consistent, high-quality results each time.

To create a template, you can ask the AI to analyze a piece of content you like and turn it into a general format. Here's an example:

Analyze the structure of this blog post and create a general template that can be used for similar posts on different topics. Identify key elements like the opening hook, main points, transitions, and conclusion. For each element, provide a brief description of its purpose and how it contributes to the overall effectiveness of the post .

You can then use this template repeatedly, plugging in different topics as needed. A detailed template might look like this:

1. Opening Hook: Start with a surprising statistic, provocative question, or brief anecdote related to the topic (2–3 sentences)

2. Introduction: Briefly explain the importance of the topic and what readers will gain from the post (1 paragraph)

3. Main Point 1: Present the first key idea, including:

• Clear explanation of the concept (2–3 sentences)
• Real-world example or case study (1–2 sentences)
• Actionable tip for implementing this idea (1–2 sentences)

4. Main Point 2: Follow the same structure as Main Point 1

5. Main Point 3: Follow the same structure as Main Point 1

6. Potential Challenges: Address 1–2 common obstacles readers might face when implementing these ideas (1 paragraph)

7. Solutions: Provide practical solutions or workarounds for the challenges mentioned (1 paragraph)

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8. Conclusion: Summarize the main points and reinforce the benefits of implementing these ideas (1 paragraph)

9. Call to Action: Encourage readers to take a specific next step, such as trying one of the techniques or sharing their own experiences (1–2 sentences)

#4: Provide Multiple Examples of Good and Bad Content to Guide your AI Assistant

Show the AI good and bad examples of what you're looking for. This helps the AI understand your expectations more clearly and learn what you're looking for and what to avoid.

To enter examples, you can copy and paste or upload PDFs and other documents to your AI assistant.

The more relevant examples you provide, the better the AI can tailor its output to your needs. For instance, you might provide five examples of good transformations (where content has been successfully turned into a template) and one example of a bad transformation.

Here's an example of how you might include examples in an AI prompt:

Here are two examples of effective email newsletters:

Example 1: [Insert or upload full text of a well-crafted newsletter]

Example 2: [Insert or upload full text of another well-crafted newsletter]

Notice the following elements in these examples:

• Conversational, friendly tone
• Short paragraphs (2–3 sentences each)
• Use of subheadings to break up content
• Inclusion of personal anecdotes
• Clear call to action at the end

Now, here's an example of a poor newsletter:

[Insert or upload full text of a poorly crafted newsletter]

Notice these issues with the poor example:

• Overly formal tone
• Long, dense paragraphs
• Lack of personal connection
• No clear structure or subheadings
• Vague or missing call to action

When you write my newsletter, please emulate the style and structure of the good examples and avoid the mistakes in the poor example. Additionally, incorporate my brand voice as described in the context I provided earlier.

#5: Iterate on and Test Your Prompt Writing and Refine It Based on the Results

Don't expect perfect results on your first try. If you're unhappy with what you get, analyze what's wrong and adjust your instructions accordingly. Initially, expect to test and adjust a prompt at least 10 times before finalizing it. 

Follow this sample framework to get started:

• Start with a basic version of the prompt
• Analyze the output for strengths and weaknesses
• Refine the prompt based on this analysis
• Test the refined prompt multiple times
• Continue this cycle until you consistently get satisfactory results

Word Choice Matters

You might need to experiment with different words or phrases in your detailed prompt. For instance, replacing “creative writer” with “professional writer” could significantly change the content style you receive. 

Similarly, specifying “write in the style of Malcolm Gladwell” or “use language appropriate for a technical white paper” can dramatically alter the output.

Use the Lever Technique to Fine-Tune Specific Aspects of AI-Generated Content

For more precise control over the output, apply the Lever Technique:

• Get an initial output from the AI

• Ask the AI to rate a specific aspect of the output on a scale (e.g., formality from 1–10)
• Request the AI to adjust that aspect to a different point on the scale

For example, if you get an email that's too formal, you might say:

On a scale of 1 to 10, with 1 being very casual and 10 being very formal, how would you rate the tone of this email?

If the AI responds with “7,” you might then say:

Please rewrite the email, adjusting the tone to a 3 on the same scale. Maintain the same information and overall structure, but make the language more conversational and friendly.

Moritz Kremb is an advanced AI prompting expert and founder of The Prompt Warrior , an email newsletter designed to help creators, marketers, and entrepreneurs excel at using AI tools. His AI Growth Kit includes content templates and effective prompts for AI. You can find him on X .

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