Scaling the Provision of Personalised Learning Support Actions to Large Student Cohorts

Project Member(s): Schulte, J., Buckingham Shum, S.

Funding or Partner Organisation: Office for Learning & Teaching (Department of Education and Training) (OLT Strategic Priority Commissioned Projects)

Start year: 2016

Summary: This project has two aims. The first is to improve the quality of student learning in large cohorts by scaling the deployment of Personal Learning Support Actions (PLSAs) within Australian HE institutions. We define PLSAs as any instructor led intervention that is designed to help students in their learning journey by recognising and acknowledging their strengths and weaknesses, and suggesting steps or mentorship interventions that are relevant to their particular situation. This term encompasses conventional actions such as the provision of feedback as well as content personalisation, advice on learning strategies, content recommendations, and visualisations. This project proposes a methodological shift in which students in large cohorts will receive frequent, personalised and relevant learning support actions derived from the combination of state‐of‐the‐art learning analytics approaches and the expertise of the instructors. The second aim of the project is to increase the maturity of learning analytics deployments in educational institutions by providing evidence‐based guidelines to move from reduced experimentations into organisational transformations.

Publications:

Pardo, A, Martínez-Maldonado, R, Buckingham Shum, S, Schulte, J, McIntyre, S, Gašević, D, Gao, J & Siemens, G 1970, 'Connecting data with student support actions in a course', Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK '17: 7th International Learning Analytics and Knowledge Conference, ACM, Simon Fraser Univ, Vancouver, CANADA, pp. 522-523.
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Pardo, A, Mirriahi, N, Martinez-Maldonado, R, Jovanovic, J, Dawson, S & Gašević, D 1970, 'Generating actionable predictive models of academic performance', Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16, the Sixth International Conference, ACM Press, Edinburgh, United Kingdom, pp. 474-478.
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FOR Codes: Information And Computing Sciences, Higher Education, Education and Training Systems not elsewhere classified, Mixed initiative and human-in-the-loop, Other education and training not elsewhere classified

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.