[1P3-OS-21] プロセス中心のシステムデザインとラーニングアナリティクス

[1P3-OS-21-03] Diagnosing and Promoting Self-Directed Investigative Learning on the Web

〇Yoshiki Sato¹, Akihiro Kashihara¹, Shinobu Hasegawa², Koichi Ota³, Ryo Takaoka⁴ (1. The University of Electro-Communications, 2. Japan Advanced Institute of Science and Technology, 3. Japan Institute of Lifelong Learning, 4. Yamaguchi University)

Keywords:Web-based investigative learning, Linked Open Data, Self-directed learning, Self-regurated learning, Reflection

In Web-based investigative learning, learners are expected to construct wider and deeper knowledge with navigating Web resources/pages. On the other hand, learners need to create a learning scenario with decomposing into related ones as sub-questions in order to elaborate the initial question. In our previous work, we have built a model of Web-based investigative learning and developed the system named iLSB, which scaffolds the investigation for learners. However, learners often investigate unrelated questions even if they use iLSB. This suggests the necessity of diagnosing learner-created scenario to present the results as feedback. On the other hand, it prevents learners from self-directed investigation. Toward this issue, we aim to diagnose learner-created scenario without prevention of self-directed investigation with LOD and to present the diagnosed results on the scenario. This paper also reports a case study. The results suggest that feedback of appropriateness of question decomposition can promote reflection and contribute to creating more appropriate scenario.