Exploration of Analytical Mechanisms in the Feedback model

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

doi:10.1016/j.procs.2019.01.037

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…The majority of these tools have a feature of authoring instructional objectives and the lack of the taxonomy of learning inside the expert modules. For that reason, in our previous work, the authors proposed to integrate the implicit and explicit intervention to ITS [2]. This tutor will select the appropriate strategy according to three criteria.…”

Section: A the Expert Model Of Smart Tutoring Systemmentioning

confidence: 99%

“…Several contributions have focused on improving the intelligent tutoring system to make it more efficient, such as the integration of the two tutors: implicit and explicit, or each tutor uses his strategies and toolbox. The choice of one of the tutors is based on the following criteria: learner profile, effective feedback and learning progress data [1]; the latter needs Learning Analytics to collect the traces that learners leave behind and the uses these traces to improve learning [2].…”

Section: Introductionmentioning

confidence: 99%

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

Hibbi,

Abdoun,

Haimoudi

2022

IJACSA

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The expert module is an essential part of the intelligent tutoring system. This module uses only declarative knowledge, excluding other types of domain knowledge: procedural and conditional. This elimination makes the expert module very delicate. To solve this issue, the authors propose to embed knowledge processing into the expert model. The contribution aims to empower the expert model via the fragmentation of the knowledge process into four categories: Analyzation, Application, Conceptualization, and Experimentation using the Bayesian Network method as an instrument for modelling expert systems in uncertain areas. According to the management of the expert system through a list of criteria, the expert module can suggest the correct type of knowledge and their following status.

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Section: A the Expert Model Of Smart Tutoring Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hibbi,

Abdoun,

Haimoudi

2022

IJACSA

Self Cite

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“…However, the problem that online learning systems lack is to optimize the learner activities during a learning process. Meanwhile, various researchers have already addressed this topic by providing several solutions [2], such as eye-tracking technology [3] clustering [4] and classification methods [5] to detect the learner style, etc. These solutions allow the detection of the learning style and subsequently deduce the list of appropriate activities.…”

Section: Introductionmentioning

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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“…Numerous researchers were investigated and studied the problems and improvement of the feedback in the intelligent tutoring system; some researchers apply the dialogical techniques to know the level of understanding the pedagogical sequence [4]. Others are concentrated to analyze the feedback model of the smart tutoring system using learning analytics [5]. Further investigation was focused on the provision of feedback using clustered solution space [6].…”

Section: Introductionmentioning

confidence: 99%

Smart Tutoring System: A Predictive Personalized Feedback in a Pedagogical Sequence

Hibbi

Abdoun

Haimoudi

2021

Int. J. Emerg. Technol. Learn.

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Feedback may be an effective interaction provided by the intelligent tutoring system. Nevertheless, the learning feedback is not easily definable, especially in front of learners with their characteristics and preferences. In this work, the authors propose to predict personalized feedback in a programming language learning context that promotes the feedback of the ITS according to the learner preferences and learner style. The recommended method uses a combination of machine learning techniques to suggest the best appropriate feedback according to learner’s preferences and characteristics. For that purpose, the predictive personalized feedback method will respect the following phases: collect the learning experience from the learning resources (LR) and learner preferences (LP), generate groups of clusters that contain the common characteristics using the k-means algorithm, and define the association rules between the four categories and their corresponding activity. Finally, generate the personalized feedback and propose the recommendation through the intervention of an expert in the field.

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Exploration of Analytical Mechanisms in the Feedback model

Cited by 8 publications

References 4 publications

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

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

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

Smart Tutoring System: A Predictive Personalized Feedback in a Pedagogical Sequence

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