Knowledge acquisition for learning analytics

Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin

Danny Y T Liu*, Deborah Richards, Phillip Dawson, Jean Christophe Froissard, Amara Atif

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.

Original languageEnglish
Title of host publicationKnowledge Management and Acquisition for Intelligent Systems - 14th Pacific Rim Knowledge Acquisition Workshop, PKAW 2016, Proceedings
EditorsHayato Ohwada, Kenichi Yoshida
Place of PublicationSwitzerland
PublisherSpringer, Springer Nature
Pages183-197
Number of pages15
Volume9806
ISBN (Print)9783319427058
DOIs
Publication statusPublished - 2016
Event14th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW2016 - Phuket, Thailand
Duration: 22 Aug 201623 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9806
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW2016
CountryThailand
CityPhuket
Period22/08/1623/08/16

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    Liu, D. Y. T., Richards, D., Dawson, P., Froissard, J. C., & Atif, A. (2016). Knowledge acquisition for learning analytics: Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin. In H. Ohwada, & K. Yoshida (Eds.), Knowledge Management and Acquisition for Intelligent Systems - 14th Pacific Rim Knowledge Acquisition Workshop, PKAW 2016, Proceedings (Vol. 9806, pp. 183-197). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9806). Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-42706-5_14