Widening the net to reduce the debt: Reducing student debt by increasing identification of completely disengaged students

Research output: Book chapter/Published conference paperConference paperpeer-review

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 languageEnglish
Title of host publicationASCILTE’s First Virtual Conference
Subtitle of host publicationAustralasian Society for Computers in Learning in Tertiary Education, Armidale University of New England Virtual Conference 30 November – 1 December 2020
EditorsSue Gregory, Steve Warburton, Mitchell Parkes
PublisherASCILITE
Pages54-59
Number of pages6
Publication statusPublished - 2020
Event37th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education: ASCILITE 2020 - Online
Duration: 30 Nov 202001 Dec 2020
https://2020conference.ascilite.org/ (conference website)
https://2020conference.ascilite.org/program/ (conference program)

Conference

Conference37th International Conference of Innovation, Practice and Research in the Use of Educational Technologies in Tertiary Education
Period30/11/2001/12/20
OtherASCILITE 2020 will run virtually. It will be held, online, on Monday 30 November and Tuesday 1 December 2020.
Internet address

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