Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model

Nguyên, Viêt Anh

doi:10.3991/ijet.v7i4.2290

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Schaul and LeCun [163] proposes an algorithm for stochastic gradient descent that automatically adjusts learning rates without the need for manual tuning, addressing issues of minibatch parallelization, sparse or orthogonal gradients, and nonsmooth loss functions. Nguyen [138] proposes a new approach to manage learner modeling in an adaptive learning system using a Bayesian network and overlay model. Nurjanah [141] discussed the design of a learner model ontology for lifelong learning to support adaptive learning systems, which classified learners' attributes into static and dynamic attributes.…”

Section: A Cluster Of General Concepts Of Adaptive Learning In E-lear...mentioning

confidence: 99%

Adaptive learning: a cluster-based literature review (2011-2022)

Fadieieva

2023

Educ. Technol. Q.

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Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact.

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Section: A Cluster Of General Concepts Of Adaptive Learning In E-lear...mentioning

confidence: 99%

Adaptive learning: a cluster-based literature review (2011-2022)

Fadieieva

2023

Educ. Technol. Q.

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“…To be able to offer this dynamic personalization, it is necessary to capture the learner's characteristics (knowledge, abilities, needs, etc.) [7][8][9][10] which are commonly represented in a student/learner model [11,12]. However, it is difficult to identify accurately these characteristics.…”

Section: Introductionmentioning

confidence: 99%

Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS

et al. 2021

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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.

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“…Cognitive maps are an academic subject object domain [9][10][11] model in the form of a semantic web with considered relations "Previous -next" between didactic units (DU) studied in the context of a certain discipline [12]. DU is realized as the "logic independent education material corresponding by volume and structure to content elements such as idea, theory and law" [13].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Testing Production Model Based on a Cognitive Map for SWI-Prolog Applications

Popov

Lazareva

2015

Int. J. Emerg. Technol. Learn.

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Abstract-In this paper, we describe the ways of improving knowledge assessment with the help of cognitive maps made during the process of working with an intelligent tutoring system, E-learning center -High-edu. Cognitive maps consist of didactic units -minimal units of knowledge about some discipline's domain. This article shows how they can be used in intelligent tutoring systems. An example of algorithm realization for forming a didactic units list from a discipline cognitive map for consistency of test task presentation determining is shown. An operator model is presented in this paper, which can be helpful for researchers and engineers using Prolog language for expert systems and knowledge management system development as well as for support of the educational process and control of student knowledge to improve the quality of electronic education.Index Terms-intelligent tutoring system, cognitive map, knowledge management, production model, SWI-Prolog.

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Toward an Adaptive Learning System Framework: Using Bayesian Network to Manage Learner Model

Cited by 5 publications

References 18 publications

Adaptive learning: a cluster-based literature review (2011-2022)

Adaptive learning: a cluster-based literature review (2011-2022)

Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS

A Knowledge Testing Production Model Based on a Cognitive Map for SWI-Prolog Applications

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