Cognitive and emotional influences on student learning

About

Our research in this area examines the relationship between established psychological concepts of learning and students’ experiences in digital learning environments. We are particularly interested in the role that the following factors play in student performance:

  • Motivation (interest, goals);
  • Self-regulation;
  • Confusion (affiliated with the national Australian Research Council funded Science of Learning Research Centre);
  • Engagement (including behavioural engagement, affective engagement, and students engagement in higher education);
  • Prior knowledge and skills; and
  • Participation.

Research grants

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; $20,000.

2016. Exploring learner misconceptions as triggers for enhanced learning. Heather Verkade, Terry Mulhern, Jason Lodge, Simon Cropper, Benjamin Rubinstein, Kristine Elliott, Michelle Livett. Learning & Teaching Initiative, The University of Melbourne. Funding: $29,000.

2014. University learning in the digital age: Investigating how students learn online. Sue Bennett, Lori Lockyer, Gregor Kennedy, Barney Dalgarno. ARC Discovery Project. Funding: $218,000.

2013. Predicting learner confusion for enhanced feedback and self-regulation. Gregor Kennedy, Lori Lockyer, Mike Timms, Rob Hester, Ottmar Lipp. This project is part of the Science of Learning Research Centre. Australian Research Council. Funding: $16 million.

Selected publications

Arguel, A., Lockyer, L., Lipp, O., Lodge, J. M., & Kennedy, G. (in press). Inside out: ways of detecting learners’ confusion for successful e-learning. Journal of Educational Computing Research.

Lodge, J. M., O'Connor, E., Burton, L., & Shaw, R. (in press). Applying cognitive science to critical thinking among higher education students. In M. Davies & R. Barnett (Eds.) Handbook of critical thinking in higher education. Palgrave.

de Barba, P., Ainley, M.D. & Kennedy, G. (2016). The role of motivation and participation for predicting performance in MOOCs. Journal of Computer Assisted Learning, 32(3), 218-231. doi: 10.1111/jcal.12130

Kennedy, G. E. & Lodge, J. M. (2016). All roads lead to Rome: Overcoming misconceptions in discovery-based digital learning tasks. Proceedings ASCILITE 2016, Adelaide.

Pachman, M., Arguel, A., Lockyer, L., Kennedy, G., & Lodge, J. M. (2016). Eye tracking and early detection of confusion in digital learning environments: Proof of concept. Australasian Journal of Educational Technology, 32(6) 58-71.

Wiseman, P., Kennedy, G. E. & Lodge, J. M. (2016). Models for understanding student engagement in digital learning environments. In proceedings ASCILITE 2016, Adelaide.

Larmar, S. & Lodge, J. M. (2014). Metacognitive capital as a predictor of first year university student retention and engagement. International Journal of the First Year in Higher Education, 5 (1), 93-105.

Corrin, L., Bennett, S., & Lockyer, L. (2013). Digital natives: Exploring the diversity of young people's experience with technology, In Huang, R., Kinshuk, & Spector, J. M. (Eds.) Reshaping Learning - The Frontiers of Learning Technologies in Global Context. New York: Springer-Verlag.

Other research themes

More information

If you are interested in undertaking research with this group please contact Kristine Elliott (email).

Back to the Educational Technology Research Group page