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Knowledge acquisition for learning analytics: comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin

Liu, Danny Y.T., Richards, Deborah, Dawson, Phillip, Froissard, Jean-Christophe and Atif, Amara 2016, Knowledge acquisition for learning analytics: comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin, in Knowledge Management and Acquisition for Intelligent Systems : 14th Pacific Rim Knowledge Acquisition Workshop, PKAW 2016 Phuket, Thailand, August 22–23, 2016 Proceedings, Springer, Cham, Switzerland, pp. 183-197, doi: 10.1007/978-3-319-42706-5_14.

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Title Knowledge acquisition for learning analytics: comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin
Author(s) Liu, Danny Y.T.
Richards, Deborah
Dawson, PhillipORCID iD for Dawson, Phillip orcid.org/0000-0002-4513-8287
Froissard, Jean-Christophe
Atif, Amara
Conference name Pacific Rim Knowledge Acquisition. Workshop (14th: 2016 : Phuket, Thailand)
Conference location Phuket, Thailand
Conference dates 22-23 Aug. 2016
Title of proceedings Knowledge Management and Acquisition for Intelligent Systems : 14th Pacific Rim Knowledge Acquisition Workshop, PKAW 2016 Phuket, Thailand, August 22–23, 2016 Proceedings
Publication date 2016
Series Lecture Notes in Computer Science
Start page 183
End page 197
Total pages 15
Publisher Springer
Place of publication Cham, Switzerland
Summary 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.
ISBN 9783319427058
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-42706-5_14
Field of Research 130306 Educational Technology and Computing
Socio Economic Objective 930203 Teaching and Instruction Technologies
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, Springer International Publishing Switzerland
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086452

Document type: Conference Paper
Collection: Centre for Research in Assessment and Digital Learning (CRADLE)
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Created: Mon, 03 Oct 2016, 14:31:11 EST

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