Abstract
Learner disengagement is a persisting issue in the Science Technology Engineering and Mathematics (STEM) subjects. Student engagement is dynamically constituted by the behavioural, cognitive and emotional dimensions of engagement in a learning environment. Although strongly linked with academic achievement, much of the details of engagement becomes lost in a retrospective measurement. Timely and microlevel data on the other hand has the ability to enrich the traditional learning analytics dataset. From a pilot study carried out at Universitat Oberta de Catalunya, where we have designed a self-reported data capture module that collects microlevel engagement data, initial results suggest the validity of the proposed approach and data. In this paper we emphasize how our approach enables better understanding of the student learning process and their characteristics such as cognitive patterns, emotional states and behaviours that leads to academic success and also enable richer feedback from teachers and informed decision making by the institution.
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Balasooriya, I., Mor, E., RodrÃguez, M.E. (2018). Engagement Analytics: A Microlevel Approach to Measure and Visualize Student Engagement. In: Caballé, S., Conesa, J. (eds) Software Data Engineering for Network eLearning Environments. Lecture Notes on Data Engineering and Communications Technologies, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-68318-8_3
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