Promoting Reflection on Question Decomposition in Web-based Investigative Learning

Abstract

In Web-based investigative learning, learners are expected to construct wider and deeper knowledge by navigating a great number of Web resources/pages. In elaborately investigating an initial question, learners are expected to decompose an initial question into related question to be further investigated. However, it is difficult for learners to conduct question decomposition in concurrence with their knowledge construction. In our previous study, we have proposed a model of Web-based investigative learning, and developed the system named interactive Learning Scenario Builder (iLSB for short). Although iLSB could promote self-directed investigative learning, learners often decompose a question into unrelated sub-questions. This suggests the necessity of promoting reflection on question decomposition by diagnosing the appropriateness of question decomposition. Toward this issue, we have proposed a method for diagnosing the appropriateness of question decomposition with Linked Open Data (LOD). In this paper, we describe an adaptive prompting with diagnosed results for reflection on question decomposition. This paper also reports a case study whose results suggest the potential for promoting reflection on improper question decomposition.