Predicting Learner Confusion for Enhanced Feedback and Self-regulation

About the project

As part of the Australian Research Council funded Science of Learning Research Centre, we are investigating learner cognition and emotion with a view to creating real time feedback for students as they work through activities in digital learning environments (DLEs). One particular focus of our investigations is learner confusion. A state of disequilibrium or confusion is often a necessary pre-condition for lasting conceptual change. We are currently examining confusion and the process of conceptual change at a physiological, cognitive and educational level. Our research methods include neuroimaging, electroencephalograms, eye tracking, data mining and randomised control trials.

A particularly important aspect of our program of research is to examine confusion in authentic interactive digital learning environments. These environments provide opportunities to examine student learning in educationally relevant settings, as opposed to the more artificial protocols required for highly controlled laboratory and psychophysiological investigations. Interactive and adaptive digital learning modules are becoming more commonly used in higher education due to a greater focus on engaging with content outside face-to-face contact sessions.  Finding ways to enhance the utility of these tools is thus of great interest to the higher education community.

One drawback of using digital learning environments (DLEs) currently is that there is limited capacity for the available tools to adapt to student thinking and feeling in the manner in which a teacher would in a face-to-face environment. As a result, DLEs often require students to be self-directed and maintain discipline and motivation. The availability of data paired with sophisticated methods for analysing and integrating these data do however provide opportunities for being better able to respond to students as they work through the learning activities they have been assigned. In this regard, we are particularly interested in combining the rigour afforded by experimental laboratory studies with the insights gleaned through the use of data mining and predictive modelling to assist students to reach a state of meaningful and lasting conceptual change.

Researchers

Gregor Kennedy (Professor)
Jason Lodge (Research Fellow)
Paula de Barba (PhD Candidate)
Sadia Nawaz (PhD Candidate)
Paul Wiseman (PhD Candidate)

Contact

Contact
Paula de Barba
Email
paula.debarba@unimelb.edu.au
Phone
(03) 8344 9904