Casey, Kevin and Azcona, David (2017) Utilizing student activity patterns to predict performance. International Journal of Educational Technology in Higher Education, 14 (4). ISSN 2365-9440

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Abstract

Apart from being able to support the bulk of student activity in suitable disciplines such as computer programming, Web-based educational systems have the potential to yield valuable insights into student behavior. Through the use of educational analytics, we can dispense with preconceptions of how students consume and reuse course material. In this paper, we examine the speed at which students employ concepts which they are being taught during a semester. To show the wider utility of this data, we present a basic classification system for early detection of poor performers and show how it can be improved by including data on when students use a concept for the first time. Using our improved classifier, we can achieve an accuracy of 85% in predicting poor performers prior to the completion of the course.

Item Type:	Article
Additional Information:	Cite this article as: Casey, K. & Azcona, D. Int J Educ Technol High Educ (2017) 14: 4. https://doi.org/10.1186/s41239-017-0044-3 © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords:	Learning Analytics; Data Mining; Virtual Learning Environments; Student behavior; Early intervention;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	10182
Identification Number:	https://doi.org/10.1186/s41239-017-0044-3
Depositing User:	Hamilton Editor
Date Deposited:	07 Nov 2018 15:14
Journal or Publication Title:	International Journal of Educational Technology in Higher Education
Publisher:	Springer
Refereed:	Yes
URI:	https://link.springer.com/
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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