Diagnosis with Linked Open Data for Question Decomposition in Web-based Investigative Learning
In Web-based investigative learning, learners are expected to construct wider and deeper knowledge by navigating a great number and variety of Web resources/pages. On the other hand, they tend to search a limited number of them, which often results in limited knowledge construction. In order to make the investigation with an initial question elaborate, learners need to decompose the question into related ones. They also need to create a scenario like a table of contents implying the questions to be investigated and their sequence. We have built a model of Web-based investigative learning, and developed the system so far. However, it remains an open problem to diagnose learner-created scenario without preventing self-directed investigation. Toward this problem, this paper proposes a diagnosis method with Linked Open Data (LOD), and reports a case study whose purpose was to evaluate the diagnosis method.
KeywordsWeb-based investigative learning Linked Open Data Self-directed learning Diagnosis
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The work was supported in part by JSPS KAKENHI Grant Number 17H01992.
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