Knowing when to target students with timely academic learning support: not a minefield with data mining

Paper


McCarthy, Elizabeth. 2017. "Knowing when to target students with timely academic learning support: not a minefield with data mining." Partridge, H., Davis, K. and Thomas, J. (ed.) 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2017). Toowoomba, Australia 04 - 06 Dec 2017 Toowoomba, Australia.
Paper/Presentation Title

Knowing when to target students with timely academic learning support: not a minefield with data mining

Presentation TypePaper
Authors
AuthorMcCarthy, Elizabeth
EditorsPartridge, H., Davis, K. and Thomas, J.
Journal or Proceedings TitleProceedings of the 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2017)
Number of Pages5
Year2017
Place of PublicationToowoomba, Australia
Web Address (URL) of Paperhttp://2017conference.ascilite.org/wp-content/uploads/2017/11/ASCILITE-2017-Proceeding.pdf
Conference/Event34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2017)
Event Details
Rank
C
C
C
Event Details
34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2017)
34th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education
Parent
Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE)
Delivery
In person
Event Date
04 to end of 06 Dec 2017
Event Location
Toowoomba, Australia
Abstract

The strategic scheduling of timely engagement opportunities with academic learning support, targeting specific student cohorts requires intentional, informed and coordinated planning. Currently these timing decisions appear to be made with a limited student focus, which considers individual course units only as opposed to having an awareness of the schedule constraints imposed by the students’ full course workload. Hence, in order to respect the full student academic workload, and maximise the quantity and quality of opportunities for students to engage with learning advisors, a means to capture and work with the composition and distribution of student full workload is needed. A data mining approach is proposed in this concise paper, where public domain information accessed from the back end HTML language of course unit information webpages is collected and consolidated in graphical form. The resulting visualisation of the students’ academic learning activities provides a quick and convenient means for academics to make informed scheduling decisions. The case study presented describes the implementation of the data mining in the context of discipline specific academic learning advisors at the University of Southern Queensland servicing three campuses under the ‘One-University’ model.

ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
390303. Higher education
Byline AffiliationsLibrary Services
Institution of OriginUniversity of Southern Queensland
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