Adaptive Interface that Provides Modeling, Coaching and Fading to Improve Revision Skill in Academic Writing

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Abstract

Revision is an important aspect of academic writing. Students may learn to revise their articles by observing already published articles in their research field. However, these articles are in their final form, so the students have no way of learning from the revision process that led to the final articles. In our previous research, we constructed a corpus of articles written by former students in our laboratory. The articles included the raw drafts and the feedback from the supervisor in the form of comments. Students can then learn from this corpus. However, it is important to recognize that the needs of novice students are different from those of more experienced students. For improving their revision skill, our main idea is to apply the cognitive apprenticeship theory which proposes several methods to teach cognitive skills to novices, such as modeling, coaching and scaffolding/fading. In modeling, the novice student is presented with a conceptual model of the processes required to accomplish a task, and the revision of an article is an example of such a task. In coaching, a student is provided with hints to help them accomplish a task. In our case, the student uploads his/her own article and is provided with hints for improving that article. In fading, the hints provided become fewer and are presented in less detail as the student’s revision skill improves. In this paper, we present the design for an interface that shows the revision process in academic writing from the initial drafts to the final drafts so that the students can learn revision skill from practical observation. In addition, the design we present is adaptive to the cognitive needs of the students.

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APA

Ocharo, H. N., & Hasegawa, S. (2018). Adaptive Interface that Provides Modeling, Coaching and Fading to Improve Revision Skill in Academic Writing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10905 LNCS, pp. 300–312). Springer Verlag. https://doi.org/10.1007/978-3-319-92046-7_27

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