Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network

Habte, Samson, Palmer-Brown, Dominic, Kang, Miao and Cai, Fang Fang (2015) Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network. Artificial Intelligence Research, 5 (2). pp. 1-13. ISSN 1927-6982

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Abstract / Description

The aim of this paper is to propose a novel method for identifying student group profiles based on student responses to a set of multiple choice questions for the purpose of constructing diagnostic feedback using snap-drift modal learning neural network. The proposed method is capable of supporting tutors without the knowledge of machine learning in identifying useful student groups and constructing diagnostic feedback. Trials were conducted and analysis of the result showed that the snap-drift modal learning neural network was able to identify distinct student groups and represented student group profiles were helpful in revealing gaps of understanding and misconceptions that facilitate construction of diagnostic feedback. Moreover, the result showed that all student responses gathered were assigned to their appropriate student group profiles and the diagnostic feedback constructed based on the identified student group profiles had a positive impact on improving the learning performance of the students.

Item Type:	Article
Uncontrolled Keywords:	Modal learning, Unsupervised learning, Snap, Drift, Diagnostic feedback, Formative assessment
Subjects:	300 Social sciences > 370 Education
Department:	School of Computing and Digital Media
Depositing User:	Bal Virdee
Date Deposited:	30 Apr 2018 14:30
Last Modified:	30 Apr 2018 14:31
URI:	https://repository.londonmet.ac.uk/id/eprint/1444

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