Affiliation:
1. Rossier School of Education, University of Southern California, Los Angeles, CA 90089, USA
Abstract
Learning Analytics applications, and their associated dashboards, are frequently used in post-secondary settings; yet, there has been limited work exploring the motivational implications of their deployment, especially for under-served student populations that are more susceptible to (perceived) negative messages about their academic performance. In this paper, I argue that Situated Expectancy-Value Theory (EVT) is well-positioned to serve as a useful lens when developing and evaluating learning analytics dashboard designs and their future development. Used in this way, SEVT can help the learning analytics community to ensure that student experiences with learning analytics are adaptively motivating, both in general and for underserved student populations more specifically.
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