Learning analytics (LA) incorporates analyzing cognitive, social and emotional processes in learning scenarios to make informed decisions regarding instructional design and delivery. Research has highlighted important roles that emotions play in learning. We have extended this field of research by exploring the role of emotions in a relatively uncommon learning scenario: learning about queer history with a multimedia mobile app. Specifically, we used an automatic facial recognition software (FaceReader 7) to measure learners’ discrete emotions and a counter‐balanced multiple‐choice quiz to assess learning. We also used an eye tracker (EyeLink 1000) to identify the emotions learners experienced while they read specific content, as opposed to the emotions they experienced over the course of the entire learning session. A total of 33 out of 57 of the learners’ data were eligible to be analyzed. Results revealed that learners expressed more negative‐activating emotions (ie, anger, anxiety) and negative‐deactivating emotions (ie, sadness) than positive‐activating emotions (ie, happiness). Learners with an angry emotion profile had the highest learning gains. The importance of examining typically undesirable emotions in learning, such as anger, is discussed using the control‐value theory of achievement emotions. Further, this study describes a multimodal methodology to integrate behavioral trace data into learning analytics research.

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Their interests include surgical education, emotion, technology and LGBTQ+ issues in education. Yang Liu is a postdoctoral fellow in the Department of Educational Psychology at the University of Alberta. His research interests include the development of technological solutions for experimental research using a broad range of programming languages and software tools. Byunghoon

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