Authors:
Angélica de Antonio
1
;
Jaime Ramírez
1
and
Julia Clemente
2
Affiliations:
1
Universidad Politécnica, Spain
;
2
Universidad de Alcalá, Spain
Keyword(s):
Intelligent Tutoring System, Student Model, Cognitive Diagnosis, Non-monotonic reasoning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Ontologies and Meta-Data Standards
;
Virtual Learning Environments
Abstract:
The advances in the educational field and the high complexity of student modelling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modelling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, in the SM cognitive diagnosis process, and we present a method based on instructional design, providing a rich diagnosis about the student’s knowledge state –especially, about the state of learning objectives reached or not-, with non-monotonic reasoning capacities, and supporting the detection and resolution of contradictions raised during the reasoning on the student’s knowledge state. T
he main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.
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