Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2019

Abstract

Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art KT modelling techniques.

Keywords

Key-value memory networks, Knowledge tracing, LSTMs, Massive open online courses, Student clustering

Discipline

OS and Networks | Software Engineering

Research Areas

Data Science and Engineering

Publication

Advances in Knowledge Discovery and Data Mining: PAKDD 2019: April 14-17, Macau: Proceedings

Volume

11440

First Page

163

Last Page

174

ISBN

9783030161453

Identifier

10.1007/978-3-030-16145-3_13

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-16145-3_13

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