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Using machine-learning and visualisation to facilitate learner interpretation of source material

Wolff, Annika; Mulholland, Paul and Zdrahal, Zdenek (2014). Using machine-learning and visualisation to facilitate learner interpretation of source material. Interactive Learning Environments, 22(6) pp. 771–788.

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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.

Item Type: Journal Article
Copyright Holders: 2012 Taylor & Francis
ISSN: 1744-5191
Keywords: inquiry learning; Web 2.0; machine-learning; intelligent tutoring; visualisation
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Mathematics, Computing and Technology
Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 34903
Depositing User: Kay Dave
Date Deposited: 30 Oct 2012 16:42
Last Modified: 22 Mar 2016 16:45
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