Socializing Pedagogical Agents for Personalization in Virtual Learning Environments

Abstract

Personalization in virtual learning environments is the system ability to provide individualization and a set of personalized services such as personalized content management, learner model, or adaptive instant interaction. The intelligent agent technology has potential regarding the creation of such personalized, adaptive and interactive e-learning applications. However, most of the available solutions have so far focused on porting existing courses with traditional teaching methods onto the virtual environments, making them available in an attractive animated interface without any fine-tuning and adaptation to the learner needs. This paper proposes a novel market-inspired collaboration model where the agents are self-interested autonomic elements collaborate to achieve a comprehensive learner model. Mentor agent makes decisions on top of a Dempster-Shafer belief accumulation to help student whenever she believes student has lost the clues and needs help. Proposed architecture is validated by applying on a sample agent augmented virtual environment designed to engage and motivate students at the lower secondary level in Singapore. Extensive experiments illustrate the effectiveness of the proposed interaction model where students have found the mentor agent as believable as a virtual teacher.