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Reading Together: A Case Study of a Collaborative Reading System in Classroom Teaching

  • Shuwen Wang
  • , Lishan Zhang
  • , Sixv Zhang
  • , Bocheng Lin
  • , Lili Liu
  • , Min Xv
  • Central China Normal University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Social annotation is an effective tool to cultivate learners' literacy skills. Besides the learners themselves, their peers and teachers are also involved in the annotating process. To facilitate effective interactions among the different types of subjects, we redesigned the social annotation activity into a four-step learning mode and developed a collaborative reading system to support this learning mode. In this system, learners can not only annotate on specific content but also interact with their peers to exchange ideas. Specifically, before the class, learners annotate individually. In the meantime, they can see their peers' annotations and make replies. During the class, each group collaboratively reflects on the individuals' before-class annotations and completes an assigned group learning task on the system. At last, the teacher gives a summarization for the whole class. Through conducting three studies in different settings and data analysis, we found that individual annotation can positively influence the learners' understanding of learning materials and the following collaborative learning process. During the before-class annotation activity, making learners able to see peers' annotations encourages the learners to think deeply and constructively. Moreover, the detailed learning feedback on the group's annotation performance is helpful for individual and collaborative learning. Now that most of the feedbacks are crafted manually or by traditional machine learning algorithms, state-of-the-art natural language processing technology like pre-trained language model has been used to automatically detect learners' cognitive engagement and will be used to automatically generate feedback in the future. Finally, we put forward two recommendations for further design of social annotation tools: (1) Using annotation filtering mechanisms to hide low-quality comments; (2) Providing automatic learning feedback and interventions to encourage learners to interact with their teammates.

源语言英语
主期刊名CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
出版商Association for Computing Machinery
ISBN(电子版)9781450394222
DOI
出版状态已出版 - 19 4月 2023
已对外发布是
活动Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023 - Hamburg, 德国
期限: 23 4月 2023 → 28 4月 2023

丛书

姓名Conference on Human Factors in Computing Systems - Proceedings

会议

会议Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023
国家/地区德国
市Hamburg
时期23/04/23 → 28/04/23

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