Title :
Learning Student Models through an Ontology of Learning Strategies
Author :
Balakrishnan, Arunkumar
Abstract :
The attempt is to understand errors in the learning behavior of a student. The approach uses an Integrated Machine Learning System (IMLS) sethat uses an ontology of machine learning strategies to decide the appropriate strategy for a situation. This Integrated Machine Learning System is used to model the learning behavior of Students. Given the teaching material the IMLS uses its ontology of Machine Learning Strategies to identify which Machine Learning strategy is applicable for each situation in the learning process. The IMLS also records the alternative learning strategies that may be suggested for the situation. These solution states are represented in Plausible Justification Trees .One of these plausible justification trees will result in the error made by the student. The wrong learning strategy used in that tree helps in identifying the learning error committed by the student.
Keywords :
intelligent tutoring systems; learning systems; ontologies (artificial intelligence); teaching; trees (mathematics); user modelling; integrated machine learning system; learning behavior; learning strategy; ontology; plausible justification trees; student model; teaching material; Cognitive science; Competitive intelligence; Computational intelligence; Computer bugs; Education; Intelligent systems; Learning systems; Machine learning; Ontologies; Problem-solving;
Conference_Titel :
Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on
Conference_Location :
Sivakasi, Tamil Nadu
Print_ISBN :
0-7695-3050-8
DOI :
10.1109/ICCIMA.2007.171