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
Chapter30
Pages387-395
Number of pages9
Volume996
DOIs
Publication statusPublished - 2019

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume996

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miner
teaching practice
Teaching
learning
education
learning environment
expert
management

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 Data Mining. AusDM 2018 (Vol. 996, pp. 387-395). (Communications in Computer and Information Science; Vol. 996). https://doi.org/10.1007/978-981-13-6661-1_30
Herbert, Katherine ; Holder, Ian. / How learning analytics becomes a bridge for non-expert data miners : Impact on higher education online teaching. Data Mining. AusDM 2018. Vol. 996 2019. pp. 387-395 (Communications in Computer and Information Science).
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Herbert, K & Holder, I 2019, How learning analytics becomes a bridge for non-expert data miners: Impact on higher education online teaching. in Data Mining. AusDM 2018. vol. 996, Communications in Computer and Information Science, vol. 996, pp. 387-395. https://doi.org/10.1007/978-981-13-6661-1_30

How learning analytics becomes a bridge for non-expert data miners : Impact on higher education online teaching. / Herbert, Katherine; Holder, Ian.

Data Mining. AusDM 2018. Vol. 996 2019. p. 387-395 (Communications in Computer and Information Science; Vol. 996).

Research output: Book chapter/Published conference paperConference paper

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AB - 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.

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Herbert K, Holder I. How learning analytics becomes a bridge for non-expert data miners: Impact on higher education online teaching. In Data Mining. AusDM 2018. Vol. 996. 2019. p. 387-395. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-13-6661-1_30