2011_10_ACII - Predicting Learner Engagement during Well-defined and Ill-defined Computer-Based Intercultural Interactions
Abstract: This article reviews the first of two experiments investigating the effect tailoring of training content has on a learner‘s perceived engagement, and to examine the influence the Big Five Personality Test and the Self-Assessment Manikin (SAM) mood dimensions have on these outcome measures. A secondary objective is to then correlate signals from physiological sensors and other variables of interest, and to develop a model of learner engagement. Self-reported measures were derived from the engagement index of the Independent Television Commission-Sense of Presence Inventory (ITC-SOPI). Physiological measures were based on the commercial Emotiv Epoc Electroencephalograph (EEG) brain-computer interface. Analysis shows personality factors to be reliable predictors of general engagement within well-defined and ill-defined tasks, and could be used to tailor instructional strategies where engagement was predicted to be non-optimal. It was also evident that Emotiv provides reliable measures of engagement and excitement in near real-time.
Goldberg, B. S., Sottilare, R. A., Brawner, K. W., & Holden, H. K. (2011, October). Predicting learner engagement during well-defined and ill-defined computer-based intercultural interactions. In International Conference on Affective Computing and Intelligent Interaction (pp. 538-547). Springer, Berlin, Heidelberg.