Knowledge Management in the Expert Model of the Smart Tutoring System

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

doi:10.1145/3386723.3387895

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…An intelligent tutoring system (ITS) is a system that provides feedback and helps learners in their learning process such as remediation in a specific field like mathematics [4], or facilitating computer theory [5]. ITS contains four components; the first one is the interface model which can help the learner in a task via a learning environment; the second one is the learner model that has the personal information of the learner, their preferences and characteristics; then, the instructional model which makes a decision about teaching method via a diagnostic process of the learner model; the last one is the expert model which has the representation of the declarative knowledge [3].…”

Section: A the Expert Model Of Smart Tutoring Systemmentioning

confidence: 99%

“…Bayesian networks (BNs) are a tool for representing uncertainty using probabilities and robust mathematical foundations. BNs correspond to the probability distributions that can be generated by products of conditional probability distributions [3]. Several models are created based on knowledge representation and reasoning.…”

Section: B the Development Of Bayesian Networkmentioning

confidence: 99%

“…This implies a lack of the taxonomy of the knowledge domain. To enhance the learner evaluation the authors [3] proposed a pedagogical solution based on the combination of three bits of knowledge: Declarative, procedural and conditional according to their status.…”

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: B the Development Of Bayesian Networkmentioning

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

Self Cite

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“…Deep learning is the mainstream technology in the recommendation system rank stage. More and more scholars [22,23] Computational Intelligence and Neuroscience cognitive intelligence, it is easier for multiple experts to share collective wisdom in the same feature space. e data cognition fusion scheme is as shown in Figure 8.…”

Section: Improve Loss Function With Multitask Learningmentioning

confidence: 99%

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation

Wang

Zhang

Wulamu

2021

Computational Intelligence and Neuroscience

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Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is often the case that user interests may change from their past routines. However, multi-perspective attention has broad horizon, which covers different characteristics of human reasoning, emotions, perception, attention, and memory. In this paper, we attempt to introduce the multi-perspective attention and sequence behaviour into multitask learning. Our proposed method offers better understanding of user interest and decision. To achieve more flexible parameter sharing and maintaining the special feature advantage of each task, we improve the attention mechanism at the view of expert interactive. To the best of our knowledge, we firstly propose the implicit interaction mode, the explicit hard interaction mode, the explicit soft interaction mode, and the data fusion mode in multitask learning. We do experiments on public data and lab medical data. The results show that our model consistently achieves remarkable improvements to the state-of-the-art method.

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A Literature Review of Tutoring Systems: Pedagogical Approach and Tools

Hibbi

2024

Lecture Notes in Networks and Systems

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Knowledge Management in the Expert Model of the Smart Tutoring System

Cited by 4 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

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation

A Literature Review of Tutoring Systems: Pedagogical Approach and Tools

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