Knowing when to target students with timely academic learning support: not a minefield with data mining
Paper
Paper/Presentation Title | Knowing when to target students with timely academic learning support: not a minefield with data mining |
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Presentation Type | Paper |
Authors | |
Author | McCarthy, Elizabeth |
Editors | Partridge, H., Davis, K. and Thomas, J. |
Journal or Proceedings Title | Proceedings of the 34th International Conference on Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education (ASCILITE 2017) |
Number of Pages | 5 |
Year | 2017 |
Place of Publication | Toowoomba, Australia |
Web Address (URL) of Paper | http://2017conference.ascilite.org/wp-content/uploads/2017/11/ASCILITE-2017-Proceeding.pdf |
Conference/Event | 34th 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 2020 | 469999. Other information and computing sciences not elsewhere classified |
390303. Higher education | |
Byline Affiliations | Library Services |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q48zq/knowing-when-to-target-students-with-timely-academic-learning-support-not-a-minefield-with-data-mining
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