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The present study examined the extent to which adaptive feedback and just-in-time writing strategy instruction improved the quality of high school students’ persuasive essays in the context of the Writing Pal (W-Pal). W-Pal is a technology-based writing tool that integrates automated writing evaluation into an intelligent tutoring system. Students wrote a pretest essay, engaged with W-Pal’s adaptive instruction over the course of four training sessions, and then completed a posttest essay. For each training session, W-Pal differentiated strategy instruction for each student based on specific weaknesses in the initial training essays prior to providing the opportunity to revise. The results indicated that essay quality improved overall from pretest to posttest with respect to holistic quality, as well as several specific dimensions of essay quality, particularly for students with lower literacy skills. Moreover, students’ scores on some of the training essays improved from the initial to revised version on the dimensions of essay quality that were targeted by instruction, whereas scores did not improve on the dimensions that were not targeted by instruction. Overall, the results suggest that W-Pal’s adaptive strategy instruction can improve the quality of students’ essays overall, as well as more specific dimensions of essay quality.
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This content will become publicly available on June 13, 2025
The impact of middle school students’ writing quality on the accuracy of the automated assessment of science content
Helping students learn how to write is essential. However, students have few opportunities to develop this skill, since giving timely feedback is difficult for teachers. AI applications can provide quick feedback on students’ writing. But, ensuring accurate assessment can be challenging, since students’ writing quality can vary. We examined the impact of students’ writing quality on the error rate of our natural language processing (NLP) system when assessing scientific content in initial and revised design essays. We also explored whether aspects of writing quality were linked to the number of NLP errors. Despite finding that students’ revised essays were significantly different from their initial essays in a few ways, our NLP systems’ accuracy was similar. Further, our multiple regression analyses showed, overall, that students’ writing quality did not impact our NLP systems’ accuracy. This is promising in terms of ensuring students with different writing skills get similarly accurate feedback.
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- Award ID(s):
- 2010483
- PAR ID:
- 10515213
- Editor(s):
- Hoadley, C; Wang, XC
- Publisher / Repository:
- International Society for the Learning Sciences
- Date Published:
- Journal Name:
- Proceedings of the 4th Annual Meeting of the International Society of the Learning Sciences 2024
- Format(s):
- Medium: X
- Location:
- Buffalo, NY
- Sponsoring Org:
- National Science Foundation
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