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. © 2011 Springer-Verlag.
CITATION STYLE
Goldberg, B. S., Sottilare, R. A., Brawner, K. W., & Holden, H. K. (2011). Predicting learner engagement during well-defined and ill-defined computer-based intercultural interactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6974 LNCS, pp. 538–547). https://doi.org/10.1007/978-3-642-24600-5_57
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