Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level

Danaparamita, Muhammad; Gaol, Ford Lumban

doi:10.14257/ijmue.2014.9.4.12

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…One possible approach to deal with uncertainty is fuzzy logic, introduced by Zadeh in 1956 as a methodology for computing and reasoning with subjective words instead of numbers [48]. Fuzzy logic is used to deal with uncertainly in real world problems caused by imprecise and incomplete data as well as human subjectivity [92]. Fuzzy logic uses fuzzy sets that involve variables with uncertain values.…”

Section: Bayesian Networkmentioning

confidence: 99%

“…The use of fuzzy logic can improve the learning environment by allowing intelligent decisions about the learning content to be delivered to the learner as well as tailored feedback that should be given to each individual learner [48]. Fuzzy logic can also diagnose the level of knowledge of the learner for a concept, and predict the level of knowledge for other concepts that are related to that concept [92]. Chrysafiadi and Virvou, in 2012, perform an empirical evaluation of the use of fuzzy logic in student modeling in a web-based educational environment for teaching computer programming.…”

Section: Bayesian Networkmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems. General Terms

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Section: Bayesian Networkmentioning

confidence: 99%

Section: Bayesian Networkmentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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“…The use of fuzzy logic can improve the learning environment by allowing intelligent decisions about the learning content to be delivered to the learner, as well as personalized feedback to be given to each learner. It is a fuzzy logic channel to diagnose the level of knowledge of the student in a concept and to predict the level of knowledge for other concepts related to that concept [31]. Some authors argue that the integration of fuzzy logic in the student's model increases student satisfaction and performance, improves the adaptability of the system and contributes to more reliable decision-making.…”

Section: A Its Architectures and Subsystemsmentioning

confidence: 99%

A Review of Intelligent Tutorial Systems in Computer and Web based Education

Alfaro

Rivera

Castaneda

et al. 2020

IJACSA

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ITS (Intelligent Tutoring Systems) are integrated and complex systems, designed and developed using approaches and methods of artificial intelligence (AI), for the resolution of problems and requirements of the teaching/learning activities in the field of education and training of students and the workforce based in computers an web based emerging resources. These systems can establish the level of student knowledge and the learning strategies used to improve the level of knowledge to support the detection and correction of student misconceptions. Their purpose is to contribute to the process of teaching and learning in a given area of knowledge, respecting the individuality of the student. In this paper, a review of intelligent tutorial systems (ITS) is presented, from the perspective of their application and usability in modern learning concepts. The methodology used was that of bibliographical review of classic works of the printed and digital literature in relation to ITS and e-Learning systems, as well as searches in diverse databases, of theses and works in universities and digital repositories. The main weakness of the research lies in the fact that the search was limited to documents published in the English, Spanish and Portuguese.

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“…In fact, in certain cases, students can accidentally give a correct answer (a guess), or inversely, they can give a wrong answer without having any flaw in the skill (a slip). Thus, the diagnosis process often deals with uncertainty [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…They proved to be successful means to handle problems with incomplete and/or uncertain data [2,11]. As far as adaptive e-learning systems are concerned, fuzzy logic and Bayesian networks are some of the most used techniques to assess and diagnose student knowledge and skills [14,15]. However, their utility remains limited when it comes to combining information from different sources and capturing potential contradictions.…”

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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Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level

Abstract: Abstract

Cited by 8 publications

References 14 publications

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

A Review of Intelligent Tutorial Systems in Computer and Web based Education

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

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