Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions
Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions作者机构：Department of Computer Science UQAM. 201 avenue du Président-Kennedy Local PK 4150 Montréal (Québec) Canada
出 版 物：《Journal of Intelligent Learning Systems and Applications》 (智能学习系统与应用（英文）)
年 卷 期：2011年第3卷第3期
页 面：139-154页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Cognitive Agents Computational Causal Modeling and Learning Emotions
摘 要：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.
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