Strategy Uptake in Writing Pal: Adaptive Feedback and Instruction

Butterfuss, Reese; Roscoe, Rod D.; McCarthy, Kathryn S.; McNamara, Danielle S.

doi:10.1177/07356331211045304

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…For different levels of students, AWE feedback was more beneficial to higher-level learners' autonomy development (Koltovskaia, 2020). For lower-level students, AWE feedback was more effective in improving their single essay quality (Butterfuss et al, 2022). In improving writing fluency and accuracy, AWE feedback was more effective for beginners and intermediate learners than advanced learners (Mohsen, 2022).…”

Section: Rq3: What Are the Impacts Of Awe Feedback On Users' Writing ...mentioning

confidence: 99%

Bibliometrically and systematically analyzing automated writing evaluation for English learning

Wu,

2024

Forum Linguist. Stud.

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Automated writing evaluation is highly discussed in artificial intelligence for English learning. It is necessary to explore the effect of automated writing evaluation on learning English as a second language. This study combined bibliometric analysis and systematic review to explore the use of automated writing evaluation for learning English as a second language. VOSviewer was used to identify the highly discussed topics, the top ten cited authors, organizations, countries, references, and sources in the studies on automated writing evaluation. Fifty-six peer-reviewed articles were selected according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. The analysis revealed that automated writing evaluation is helpful, but its effectiveness varies according to the types of feedback, and it cannot replace the role of human feedback yet. Teachers’ roles are significant in integrating automated writing evaluation into the classroom. Future research could focus on the specific ways to integrate automated writing evaluation into the classroom better.

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Section: Rq3: What Are the Impacts Of Awe Feedback On Users' Writing ...mentioning

confidence: 99%

Bibliometrically and systematically analyzing automated writing evaluation for English learning

Wu,

2024

Forum Linguist. Stud.

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“…An important implication of [7] and similar work [30,41] is that AWE can feasibly address greater variance in student writing. Crossley and colleagues utilized NLP metrics to characterize essays-the same indices and tools that underlie several AWE systems (e.g., Writing Pal; [49][50][51][52][53]). Additional human corpus judgments or annotations were not required.…”

Section: Recognizing Writing Variance In Automated Writing Evaluationmentioning

confidence: 99%

An NLP-Based Exploration of Variance in Student Writing and Syntax: Implications for Automated Writing Evaluation

Goldshtein,

Alhashim,

Roscoe

2024

Computers

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In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they must be able to consider similar kinds of variance. The current study employed natural language processing (NLP) to explore variance in syntactic complexity and sophistication across clusters characterized in a large corpus (n = 36,207) of middle school and high school argumentative essays. Using NLP tools, k-means clustering, and discriminant function analysis (DFA), we observed that student writers employed four distinct syntactic patterns: (1) familiar and descriptive language, (2) consistently simple noun phrases, (3) variably complex noun phrases, and (4) moderate complexity with less familiar language. Importantly, each pattern spanned the full range of writing quality; there were no syntactic patterns consistently evaluated as “good” or “bad”. These findings support the need for nuanced approaches in automated writing assessment while informing ways that AWE can participate in that process. Future AWE research can and should explore similar variability across other detectable elements of writing (e.g., vocabulary, cohesion, discursive cues, and sentiment) via diverse modeling methods.

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“…This struggle is all the more acute for EFL learners who must complete literary work in English language. Besides, students may not receive sufficient writing instruction and feedback from teachers in a classroom context (Butterfuss et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding English as a Foreign Language Students’ Idea Generation Strategies for Creative Writing With Natural Language Generation Tools

Woo¹,

Wang

Susanto

et al. 2023

Journal of Educational Computing Research

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Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students’ use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL students interact with NLG tools to generate ideas. This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for idea generation. Four Hong Kong secondary school students attended workshops where they learned to write stories comprising their own words and words generated by NLG tools. After the workshops, they answered questions to reflect on their writing experience with NLG tools. In a thematic analysis of the written reflections, we found students may have existing ideas when searching for ideas and evaluating ideas with NLG tools. Students showed some aversion to ideas generated by NLG tools and selected NLG tools that generated a greater quantity of ideas. The findings inform our understanding of EFL students’ concerns when using NLG tools for idea generation and can inform educators’ instruction to implement NLG tools for classroom creative writing.

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Strategy Uptake in Writing Pal: Adaptive Feedback and Instruction

Cited by 17 publications

References 36 publications

Bibliometrically and systematically analyzing automated writing evaluation for English learning

Bibliometrically and systematically analyzing automated writing evaluation for English learning

An NLP-Based Exploration of Variance in Student Writing and Syntax: Implications for Automated Writing Evaluation

Understanding English as a Foreign Language Students’ Idea Generation Strategies for Creative Writing With Natural Language Generation Tools

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