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.

Local appearance descriptors are widely used on facial emotion recognition tasks. With these descriptors, image filters, such as Gabor wavelet or local binary patterns (LBP) are applied on the whole or specific regions of the face to extract facial appearance changes. But it is also clear that beside feature descriptor; choice of suitable learning method that integrates feature novelty is vital. The multiple kernels learning (MKL) framework reportedly shows promising performances on problems of this nature. However, most MKL studies in object recognition domain provide conflicting reports about recognition performances of MKL. We resolve such conflicts by motivating a comparative analysis of MKL using appearance descriptors for facial emotion recognition-in challenging learning setting. Moreover, we introduce a simulated learning emotion (SLE) dataset for the first time in model performance evaluation. We conclude that given sufficient training elements (examples) with efficient feature descriptor, the rapper methods of Semi-infinite programming MKL (SIP-MKL) and SimpleMKL frameworks are relatively efficient on facial emotion recognition task, compare to other kernel combination schemes. Nevertheless we opine that average MKL performance accuracy, especially on learning facial emotion dataset, remains unsatisfactory (around 56%).

This chapter describes how a machine vision approach could be utilized for tracking learning feedback information on emotions for enhanced teaching and learning with Intelligent Tutoring Systems (ITS). The chapter focuses on analyzing learners’ emotions to show how affective states account for personalization or traceability for learning feedback. The chapter achieves this goal in three ways: (1) by presenting a comprehensive review of adaptive educational learning systems, particularly inspired by machine vision approaches; (2) by proposing an affective model for monitoring learners’ emotions and engagement with educational learning systems; (3) by presenting a case-based technique as an experimental prototype for the proposed affective model, where students’ facial expressions are tracked in the course of studying a composite video lecture. Results of the experiments indicate the superiority of such emotion-aware systems over emotion-unaware ones, achieving a significant performance increment of 71.4%.

No abstract

User experience (UX) measurement has become a powerful component in determining the usability success or failure of products or services that are marketed via e-business channels. Succcess in the e-business does not only depend on building stellar software interfaces but also on competitive receptiveness to customers experience or feedback. Only e-businesses that can effectively measure the UX to forecast and understand the future are able to stay afloat and not get drown in the highly competitive market. The development of various UX metrics and measurement techniques have helped to quantify user feedack but most of these rely on different contextual assumptions. As a result, choosing appropriate UX techniques that match a particular business need becomes difficult for most e-business concerns. This chapter provides an overview of recent UX measurement techniques that are relevant to the e-business settings in the Web 2.0 era. The objective is to elaborate on what tools that have been employed in literature to measure UX and possibly how these can be employed in practice.