Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions

Usef Faghihi, Philippe Fournier-Viger, Roger Nkambou, Pierre Poirier

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Department of Computer Science, UQAM. 201, avenue du Président-Kennedy, Local PK 4150, Montréal (Québec), Canada.

DOI: 10.4236/jilsa.2011.33016                

Abstract

This paper describes a computational model for the implementation of causal learning in cognitive agents. The Conscious Emotional Learning Tutoring System (CELTS) is able to provide dynamic fine-tuned assistance to users. The integration of a Causal Learning mechanism within CELTS allows CELTS to first establish, through a mix of datamining algorithms, gross user group models. CELTS then uses these models to find the cause of users' mistakes, evaluate their performance, predict their future behavior, and, through a pedagogical knowledge mechanism, decide which tutoring intervention fits best.

Keywords

Cognitive Agents, Computational Causal Modeling and Learning, Emotions

Conflicts of Interest

The authors declare no conflicts of interest.

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