This article evaluates some of the underlying assumptions of a data analytics initiative being undertaken at an Australian university, which provide student support staff with lists of students who have enrolled in program components (classes) different from the plan prescribed in the curriculum (e.g., out of sequence, not on the plan). This study was undertaken with the assumption that these ‘inappropriate’ enrolments might negatively impact student progression and success. The analysis suggests that student progression is significant negatively affected; in particular, students can be prevented from studying full-time, extending the time needed to complete their program. However, the impact on student success was found to be minimal. The findings help to demonstrate the impact of the data initiative, and the value of continuing and expanding its use into the future.

This article describes data analytics action research initiatives that have gained traction at a university in Western Australia, with a focus on the return on investment of improving retention. In this report we focus on how actionable data can be provided and insights supported to the right stakeholders at various levels of the organization. The article will detail experiences with three levels of initiatives that have been implemented at the university: high level insights being delivered to faculty staff, the provision of integrated reports for lecturing staff, and the curation of ‘at-risk’ student lists for triage by student support staff. In conclusion, higher education institutions may consider five dimensions of learning and teaching operations: finding and selecting students; knowing learners and their expectations; just-in-time services, content, mentoring and support; anytime, anywhere accessibility; and global connectivity.

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