Mining interactions in immersive learning environments for real-time student feedback

Gregor Kennedy, Ioanna Ioannou, Yun Zhou, James Bailey, Stephen O'Leary

Abstract


The analysis and use of data generated by students’ interactions with learning systems or programs – learning analytics – has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in students’ online interactions that can be meaningfully interpreted and then fed back to students in a way that supports their learning. In this paper we present an investigation of how the digital data records of students’ interactions within an immersive 3D environment can be mined, modeled and analysed, to provide real-time formative feedback to students as they complete simulated surgical tasks. The issues that emerged in this investigation as well as areas for further research and development are discussed.


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DOI: http://dx.doi.org/10.14742/ajet.700