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

McCarthy, Elizabeth (2017) Knowing when to target students with timely academic learning support: not a minefield with data mining. In: 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education: Me, Us, IT! (ASCILITE 2017), 4-6 Dec 2017, Toowoomba, Australia.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: This work is made available under a Creative Commons Attribution 4.0 International licence.
Faculty / Department / School: Current - Division of Academic Services - Library
Date Deposited: 15 Feb 2018 01:36
Last Modified: 23 May 2018 03:45
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
13 Education > 1301 Education Systems > 130103 Higher Education
Socio-Economic Objective: C Society > 93 Education and Training > 9301 Learner and Learning > 930101 Learner and Learning Achievement
URI: http://eprints.usq.edu.au/id/eprint/33658

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