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Using machine-learning and visualisation to facilitate learner interpretation of source material
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DOI：10.1080/10494820.2012.731003.png)
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Abstract
En 中文
This paper describes an approach for supporting inquiry learning from source materials, realised and tested through a tool-kit. The approach is optimised for tasks that require a student to make interpretations across sets of resources, where opinions and justifications may be hard to articulate. We adopt a dialogue-based approach to learning whereby the student creates an external representation to reflect their current understanding of the task. This in turn prompts immediate feedback, designed to help the learner to see patterns or irregularities in their current perspective. Through the on-going feedback, the student is encouraged to make incremental changes to achieve a coherent outcome. In this approach, learners are encouraged to generate meaningful responses for themselves, rather than relying on feedback which explicitly provides an answer. This is aimed at prompting deeper processing and understanding of source materials in the context of the given learning goal.
Keywords:
intelligent tutoring
inquiry learning
Web 2.0
machine-learning
visualisation
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