Emotion Recognition Using Multiple Kernel Learning toward E-learning Applications

Abstract

Adaptive Educational Hypermedia (AEH) e-learning models aim to personalize educational content and learning resources based on the needs of an individual learner. The Adaptive Hypermedia Architecture (AHA) is a specific implementation of the AEH model that exploits the cognitive characteristics of learner feedback to adapt resources accordingly. However, beside cognitive feedback, the learning realm generally includes both the affective and emotional feedback of the learner, which is often neglected in the design of e-learning models. This article aims to explore the potential of utilizing affect or emotion recognition research in AEH models. The framework is referred to as Multiple Kernel Learning Decision Tree Weighted Kernel Alignment (MKLDT-WFA). The MKLDT-WFA has two merits over classical MKL. First, the WFA component only preserves the relevant kernel weights to reduce redundancy and improve the discrimination for emotion classes. Second, training via the decision tree reduces the misclassification issues associated with the SimpleMKL. The proposed work has been evaluated on different emotion datasets and the results confirm the good performances. Finally, the conceptual Emotion-based E-learning Model (EEM) with the proposed emotion recognition framework is proposed for future work.