Title
Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction
Abstract
We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student's behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student's knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.
Year
DOI
Venue
2014
10.1016/j.eswa.2013.07.077
Expert Syst. Appl.
Keywords
Field
DocType
pedagogic diagnosis module,conflict solver module,pedagogic diagnosis,student modeling approach,intelligent virtual environment,non-monotonic diagnosis,new knowledge,diagnosis rule,student model,student ontology,diagnosis process,proposed student model
Monotonic function,TUTOR,Ontology,Virtual machine,Intelligent tutoring system,Computer science,Artificial intelligence,Solver,Machine learning,Contradiction
Journal
Volume
Issue
ISSN
41
2
0957-4174
Citations 
PageRank 
References 
8
0.52
12
Authors
3
Name
Order
Citations
PageRank
Julia Clemente1373.71
Jaime Ramírez211416.36
Angélica de Antonio316127.23