What is the role of emotions in learning analytics?
Dr Bart Rienties & Prof Denise Whitelock (Open University), Prof Steven Warburton (University of Surrey)
With the increased availability of large datasets, powerful analytics engines, and visualisations of analytics results, educational institutions may be able to monitor, unpack and understand the learning processes of their learners. A recent Learning Analytics Review by Rienties and Alden Rivers (2014) indicated that an increasing body of research has found that emotions are key “drivers” for learning. Emotions play a critical role in the learning and teaching process because learners’ feelings impact motivation, self-regulation and academic achievement. In traditional learning environments, such as lectures, seminars, and tutorials, there is an increased recognition that emotions are important factors affecting students’ learning. However, in blended and online contexts and when considering learning analytics, in particular, limited research is available on how emotions impact learning.
Measuring emotions in learning analytics brings significant epistemological, ontological, theoretical and practical challenges. Researchers’ assumptions about the nature of reality, the knower and the knowledge that guides the study of emotions and personal orientations will influence the collection and interpretation of these data (Buckingham Shum & Deakin Crick, 2012; Tempelaar, Rienties, & Giesbers, 2014). With increased affordances of technologies to continuously measure emotions (e.g., facial and voice expressions with tablets and smart phones), it might become feasible to monitor learners’ emotions on a real-time basis in the near future.
Using Garrison’s (2011) adjusted Community of Inquiry framework, we aim to provide and test a conceptual framework for learning analytics researchers at two universities (Open University UK, University of Surrey) to unpack and understand the role of emotional presence in blended and online learning. In University of Surrey, we propose to equip a selected group of students with fitness trackers, and implement simultaneously psychometric instruments to measure emotional responses in/outside the classroom, and link this with VLE tracking behaviour. In the Open University UK, we will conduct an experiment using OpenMentor to investigate the role of emotions arising from feedback. By measuring and conceptualising the impact of emotions on students attitudes, behaviour and cognition, we aim to unpack how emotions can be tracked in learning analytics with and without smart devices.
Buckingham Shum, S., & Deakin Crick, R. (2012). Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics.Paper presented at the 2nd International Conference on Learning Analytics & Knowledge, Vancouver, British Columbia.
Rienties, B., & Alden Rivers, B. (2014). Measuring and Understanding Learner Emotions: Evidence and Prospects LACE review papers (Vol. 1). Milton Keynes: LACE.
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2014). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context.Computers in Human Behavior. doi: 10.1016/j.chb.2014.05.038
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