Abstract
Student Retention and Attrition guidelines are part of the Federal Government’s performance based funding framework. One of the recommendations from the Higher Education Standards Panel review is to consider changing students’ enrolment prior to census date when a certain level of engagement is not met. This study investigates this recommendation by trialing and testing a model to see if completely disengaged students are able to be retrospectively identified as at risk of failing all subjects. Using learning analytics alone to create a predictive model at scale proved to be very difficult. When applied to session 1 of 2019, even the strictest criteria included five false positives out of 17 identified students. There is promise, however, that a hybrid model of learning analytics with additional oversight from teaching staff could be a solution, but this needs further research.
Original language | English |
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Title of host publication | ASCILTE’s First Virtual Conference |
Subtitle of host publication | Australasian Society for Computers in Learning in Tertiary Education, Armidale University of New England Virtual Conference 30 November – 1 December 2020 |
Editors | Sue Gregory, Steve Warburton, Mitchell Parkes |
Publisher | ASCILITE |
Pages | 54-59 |
Number of pages | 6 |
Publication status | Published - 2020 |
Event | 37th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education: ASCILITE 2020 - Online Duration: 30 Nov 2020 → 01 Dec 2020 https://2020conference.ascilite.org/ (conference website) https://2020conference.ascilite.org/program/ (conference program) |
Conference
Conference | 37th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education |
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Period | 30/11/20 → 01/12/20 |
Other | ASCILITE 2020 will run virtually. It will be held, online, on Monday 30 November and Tuesday 1 December 2020. |
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