A hybrid intelligence-aided approach to affect-sensitive e-learning

Abstract

E-Learning has revolutionized the delivery of learning through the support of rapid advances in Internet technology. Compared with face-to-face traditional classroom education, e-learning lacks interpersonal and emotional interaction between students and teachers. In other words, although a vital factor in learning that influences a human's ability to solve problems, affect has been largely ignored in existing e-learning systems. In this study, we propose a hybrid intelligence-aided approach to affect-sensitive e-learning. A system has been developed that incorporates affect recognition and intervention to improve the learner's learning experience and help the learner become better engaged in the learning process. The system recognizes the learner's affective states using multimodal information via hybrid intelligent approaches, e.g., head pose, eye gaze tracking, facial expression recognition, physiological signal processing and learning progress tracking. The multimodal information gathered is fused based on the proposed affect learning model. The system provides online interventions and adapts the online learning material to the learner's current learning state based on pedagogical strategies. Experimental results show that interest and confusion are the most frequently occurring states when a learner interacts with a second language learning system and those states are highly related to learning levels (easy versus difficult) and outcomes. Interventions are effective when a learner is disengaged or bored and have been shown to help learners become more engaged in learning.