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首页> 外文会议>International conference on affective computing and intelligent interaction;ACII 2011 >Predicting Learner Engagement during Well-Defined and Ill-Defined Computer-Based Intercultural Interactions
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Predicting Learner Engagement during Well-Defined and Ill-Defined Computer-Based Intercultural Interactions

机译：在定义良好和定义不佳的基于计算机的跨文化互动中预测学习者的参与度

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摘要

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.
机译：本文回顾了两个实验中的第一个，该实验研究了培训内容的定制对学习者感知的参与的影响，并考察了“五人格测试”和“自我评估人体模型”（SAM）情绪维度对这些结果指标的影响。第二个目标是将来自生理传感器的信号与其他感兴趣的变量相关联，并开发学习者参与度的模型。自我报告的衡量指标来自独立电视委员会的在场意识调查（ITC-SOPI）的参与度指数。生理措施是基于商业Epoc脑电图仪（EEG）的脑机接口。分析表明，人格因素是在定义明确和定义不明确的任务中一般参与度的可靠预测指标，并且可以用于调整预测参与度不理想的教学策略。同样明显的是，Emotiv提供了接近实时的可靠参与度和兴奋度度量。

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