Effective Diagnostic Feedback for Online Multiple-Choice Questions

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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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.316-326, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_33〉
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Ruisheng Guo, Dominic Palmer-Brown, Sin Lee, Fang Cai. Effective Diagnostic Feedback for Online Multiple-Choice Questions. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.316-326, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_33〉. 〈hal-01521425〉

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