Sidney D’Mello|Art Graesser
AUTOMATIC DETECTION OF LEARNER'S AFFECT FROM GROSS BODY LANGUAGE
University of Memphis|University of Memphis
explored reliability detecting learners affect monitoring gross body language body position arousal interactions intelligent tutoring system called autotutor training validation data affective states collected learning session autotutor learners affective states ie emotions rated learner peer two trained judges automated body pressure measurement system used capture pressure exerted learner seat back chair tutoring session extracted two sets features pressure maps first set focused average pressure exerted along magnitude direction changes pressure emotional experiences second set features monitored spatial temporal properties naturally occurring pockets pressure constructed five data sets temporally integrated affective judgments two sets pressure features first four datasets corresponded judgments learner peer two trained judges whereas final data set integrated judgments two trained judges machinelearning experiments yielded affect detection accuracies 73 72 70 83 74 respectively chance 50 detecting boredom confusion delight flow frustration neutral accuracies involving discriminations two three four five affective states excluding neutral 71 55 46 40 chance rates 50 33 25 20 respectively