Our research in this area investigates how learning analytics data can be leveraged to support student learning and teaching practice. Of particular interest is the use of leaning analytics data to provide timely individualised feedback to students, and to return meaningful data to teachers to inform curriculum review.
We also use learning analytic techniques to interrogate large data sets captured from massive open online courses (MOOCs) and other digital learning environments, with the aim of identifying factors that predict student performance.
Research in this area is closely affiliated with the University’s Learning Analytics Research Group, which investigates learning through the analysis of large data sets of staff and students' interactions in electronic learning environments.
2017. Learning reflections: Designing interventions to promote learning in museums. Gregor Kennedy, Cameron Hocking, Eduardo Araujo Oliveira, Paula de Barba, Kristine Elliot, Ben Cleveland. The University of Melbourne and Museum Victoria, McCoy Project. Funding; $20,00
2016. Determining students’ assessment feedback preferences for personal analytics solutions. Linda Corrin, Paula de Barba, Gregor Kennedy. Office for Learning and Teaching seed grant. Funding: $40,000.
2015. Conducting and teaching curriculum review with learning analytics. Linda Corrin and Victoria Millar. Learning & Teaching Initiative, The University of Melbourne. Funding: $7,500.
2014. Completing the Loop: Returning meaningful learning analytics data to teachers. Gregor Kennedy, Linda Corrin, Shane Dawson, Lori Lockyer plus colleagues. Office for Learning and Teaching competitive grant. Funding: $218,000.
2014. Student retention and learning analytics: a snapshot of current Australian practices and a framework for advancement. Led by Shane Dawson. Office for Learning and Teaching commissioned project. Funding: $248,000.
Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gaševic ́, D., Mulder, R., Williams, D., Dawson, S., & Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics. In Proceedings of the 6th International Conference on Learning Analytics and Knowledge. New York: ACM.
Kennedy, G., Coffrin, C., de Barba, P., & Corrin, L. (2015). Predicting success: how learners’ prior knowledge, skills and activities predict MOOC performance. In P. Blikstein, A. Merceron, & G. Siemens (Eds.), Proceedings of the 5th International Conference on Learning Analytics and Knowledge (pp. 136-140). New York: ACM.
Milligan, S. (2015). Crowd-sourced learning in MOOCs: learning analytics meets measurement theory. In P. Blikstein, A. Merceron, & G. Siemens (Eds.), Proceedings of the 5th International Conference on Learning Analytics and Knowledge (pp. 151-155). New York: ACM.
Milligan, S.K. & Griffin, P. (2015). Mining a MOOC: what our MOOC taught us about professional learning, teaching and assessment. In E. Mckay & J. Linarcic (Eds.), Macro-Level Learning through Massive Open Online Courses ( MOOCs): Strategies and Predictions for the Future. Advances in Educational Technologies & Instructional Design Book Series, IGI Global.
Coffrin, C., Corrin, L., De Barba, P., & Kennedy, G. (2014). Visualizing patterns of student engagement and performance in MOOCs. In M. Pistilli, J. Willis, D. Koch, K. Arnold, S. Teasley, & A. Pardo (Eds.), Proceedings of the 4th International Conference on Learning Analytics and Knowledge (pp. 83-92). New York: ACM.
Olmos, M. & Corrin, L. (2012). Learning Analytics: A Case Study of the Process of Designs of Visualizations, Journal of Asynchronous Learning Networks, 16(3), 39-49.
Olmos, M. & Corrin, L. (2012). Academic analytics in a medical curriculum: Enabling educational excellence. Australasian Journal of Educational Technology, 28(1), 1-15.
Other research themes
If you are interested in undertaking research with this group please contact Kristine Elliott (email).