Title

Affect recognition for Web 2.0 intelligent e-tutoring systems: Exploration of students emotional feedback

Document Type

Book Chapter

Publisher

Information Science Reference

Faculty

Faculty of Health, Engineering and Science

School

School of Engineering/Centre for Communications and Electronics Research

RAS ID

17164

Comments

This chapter was originally published as: Akputu, O. K., Seng, K. P. , & Lee, Y. L. (2013). Affect recognition for Web 2.0 intelligent e-tutoring systems: Exploration of students emotional feedback. In J. Pelet (Eds.). E-Learning 2.0 technologies and web applications in higher education (pp. 188-215). Hershey, PA: Information Science Reference. Original book available here

Abstract

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%.

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