Effective Diagnostic Feedback for Online Multiple-Choice Questions - Artificial Intelligence Applications and Innovations - Part I (AIAI 2012) Access content directly
Conference Papers Year : 2012

Effective Diagnostic Feedback for Online Multiple-Choice Questions

Ruisheng Guo
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Dominic Palmer-Brown
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Sin Wee Lee
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Fang Fang Cai
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Abstract

When students attempt MCQs (Multiple-Choice Questions) they generate invaluable information which can form the basis for understanding their learning behaviours. In this research, the information is collected and automatically analysed to provide customized, diagnostic feedback to support students’ learning. This is achieved within a web-based system, incorporating the SDNN (Snap-drift neural network) based analysis of students’ responses to MCQs. This paper presents the results of a large trial of the method and the system which demonstrates the effectiveness of the feedback in guiding students towards a better understanding of particular concepts.
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hal-01521425 , version 1 (11-05-2017)

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Ruisheng Guo, Dominic Palmer-Brown, Sin Wee Lee, Fang Fang Cai. Effective Diagnostic Feedback for Online Multiple-Choice Questions. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.316-326, ⟨10.1007/978-3-642-33409-2_33⟩. ⟨hal-01521425⟩
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