Possibilistic networks for uncertainty knowledge processing in student diagnosis

In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation

Knowledge-based diagnostic system with probabilistic approach

This paper presents a knowledge learning diagnostic approach implemented in an educational system. Probabilistic inference is used here to diagnose knowledge understanding level and to reason about probable cause of learner’s misconceptions. When one learner takes an assessment, the system use probabilistic reasoning and will advice the learner about the most appropriate error cause and will also provide, the conforming part of theory which treats errors related to his misconceptions

Uncertainty management using bayesian networks in student knowledge diagnosis

In intelligent tutoring systems, student or user modeling implies dealing with imperfect and uncertain knowledge. One of the artificial intelligence techniques used for uncertainty management is that of Bayesian networks. This paradigm is recommended in the situation when exist dependencies between data and qualitative information about these data. In this work we present a student knowledge diagnosis model based on representation with Bayesian networks. The educational system incorporate a multimedia interface for accomplishes the testing tools. The results of testing sessions are represented and interpreted with probability theory in order to ensure an adapted support for the student. The aims of the computer assisted application that contains this diagnose module are to support the student in personalized learning process and errors explanation

Pedagogical knowledge model based on conceptual graphs and ontology

Intelligent educational systems are knowledge-based systems (KBS) they can be developed by a generic knowledge-based system development methodology. In this paper, we present an ontology-based approach for formalizing different knowledge types. The formalism is based upon conceptual graphs. A priority concern to all research work in adaptive education is that of finding an appropriate representation for pedagogical knowledge. For implementation, we use the CoGITaNT environment (Conceptual Graphs Integrated Tools allowing Nested Typed graphs), a library of C++ classes (open-sources, developed by LIRMM CNRS, France) allowing the development of applications based on the CG knowledge representation scheme