Bayesian Network Modelling for Improved Knowledge Management of the Expert Model in the Intelligent Tutoring System

Hibbi, Fatima-Zohra; Abdoun, Otman; Haimoudi, El Khatir

doi:10.14569/ijacsa.2022.0130624

Cited by 2 publications

(1 citation statement)

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“…Static features include information such as email address, age, and native language and are defined before the learning processes begin. However, dynamic features come from the learners' behaviour during interaction with the system [11]. The Fig.…”

Section: Learner Modulementioning

confidence: 99%

Genetic Approach for Improved Prediction of Adaptive Learning Activities in Intelligent Tutoring System

Hibbi,

Abdoun,

Haimoudi

2023

IJACSA

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The intelligent tutoring system registers the reference data of the learners in a database. This data is stored for later use in the instructional module. Designing a student model is not an easy task. It is first necessary to identify the knowledge acquired by the learner, then identify the learner's level of understanding of the functionality and finally identify the pedagogical strategies used by the learner to solve a problem. These elements must be taken into account in the development of the learner model. Learner characteristics must be considered in several forms. To build an effective learner model, the system must take into consideration both static (Learner preferences) and dynamic (Compartmental action) student characteristics. The objective of the article is to work out the learner model of the intelligent tutoring system by suggesting a new learning path. This proposal is based on the constructivist approach and the activist style (based on experimentation). According to the KOLB model, the authors propose a list of pedagogical activities depending on the learners' profile. Based on the learners' actions, the system reduces the list of activities based on two criteria: the learner's preference and the presence of one or more activities based on the activist style using genetic algorithm as an evolutionary algorithm. The results obtained led us to improve the learning process through a new conception of the ITS learner model.

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Section: Learner Modulementioning

confidence: 99%