Predicting Learner Engagement during Well-Defined and Ill-Defined Computer-Based Intercultural Interactions

  • Benjamin S. Goldberg
  • Robert A. Sottilare
  • Keith W. Brawner
  • Heather K. Holden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6974)


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.


learner engagement well-defined tasks ill-defined tasks EEG 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benjamin S. Goldberg
    • 1
  • Robert A. Sottilare
    • 1
  • Keith W. Brawner
    • 1
  • Heather K. Holden
    • 1
  1. 1.United States Army Research Laboratory Human Research and Engineering Directorate Simulation and Training Technology CenterOrlandoUSA

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