Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS
Source abstract
Abstract
This work is related to the diagnosis process in intelligent tutoring systems (ITS). This process is usually a complex task that relies on imperfect data. Indeed, learning data may suffer from imprecision, uncertainty, and sometimes contradictions. In this paper, we propose Diag-Skills a diagnosis model that uses the theory of belief functions to capture these imperfections. The objective of this work is twofold: first, a dynamic diagnosis of the evaluated skills, then, the prediction of the state of the non-evaluated ones. We conducted two studies to evaluate the prediction precision of Diag-Skills. The evaluations showed good precision in predictions and almost perfect agreement with the instructor when the model failed to predict the effective state of the skill. Our main premise is that these results will serve as a support to the remediation and the feedbacks given to the learners by providing them a proper personalization.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Intelligent Tutoring Systems and Adaptive Learning · AI-based Problem Solving and Planning · Topic Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Department, Université de Badji Mokhtar, Annaba 23000, Algeria
CNRS Heudiasyc UMR 7253, Université de Technologie de Compiègne, Alliance Sorbonne Université, 60200 Compiègne, France
Institut de Recherche en Informatique de Toulouse, Université de Toulouse 3, 31000 Toulouse, France
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