TITLE:
Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions
AUTHORS:
Usef Faghihi, Philippe Fournier-Viger, Roger Nkambou, Pierre Poirier
KEYWORDS:
Cognitive Agents, Computational Causal Modeling and Learning, Emotions
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.3 No.3,
August
5,
2011
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.