How learning analytics becomes a bridge for non-expert data miners: Impact on higher education online teaching

Katherine Herbert, Ian Holder

Research output: Book chapter/Published conference paperConference paper

Abstract

This paper builds on the current studies on data mining’s potential benefits to online learning environments. Many Teaching Academics who are non-experts in data mining techniques however are not able to take advantage of these potential benefits. The objective of this paper is to illustrate how learning analytics is bridging the gap between data mined from Learning Management Systems and teaching practice development in higher education, specifically for Teaching Academics who recently transitioned into online teaching. The authors suggest that bridging this gap is an essential step in the development of online teaching practices and online courses. A customised Dashboard that curates data mined from a university’s LMS is discussed, showcasing the impact on the practices of Teaching Academics. The results from the preliminary exploration suggest that learning analytics can bridge the gap between expert and non-experts of data mining techniques and can become a valuable tool for teaching practice development.
Original languageEnglish
Title of host publicationData Mining. AusDM 2018
Subtitle of host publicationCommunications in Computer and Information Science
EditorsRafiqul Islam, Yun Sing Koh, Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Zahidul Islam
PublisherSpringer Singapore
Chapter30
Pages387-395
Number of pages9
Volume996
ISBN (Electronic)9789811366611
ISBN (Print)9789811366604
DOIs
Publication statusPublished - 16 Feb 2019
Event16th Australasian Data Mining Conference: AusDM 2018 - Charles Sturt University Bathurst, Bathurst, Australia
Duration: 28 Nov 201830 Nov 2018
Conference number: 16th
http://ausdm18.ausdm.org/

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume996

Conference

Conference16th Australasian Data Mining Conference
CountryAustralia
CityBathurst
Period28/11/1830/11/18
OtherThe Australasian Data Mining Conference (AusDM) has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.
Internet address

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  • Cite this

    Herbert, K., & Holder, I. (2019). How learning analytics becomes a bridge for non-expert data miners: Impact on higher education online teaching. In R. Islam, Y. Sing Koh, Y. Zhao, G. Warwick, D. Stirling, C-T. Li, & Z. Islam (Eds.), Data Mining. AusDM 2018: Communications in Computer and Information Science (Vol. 996, pp. 387-395). (Communications in Computer and Information Science; Vol. 996). Springer Singapore. https://doi.org/10.1007/978-981-13-6661-1_30