Modeling Personalized Learning Styles in a Web-Based Learning System

Hsu, Chia-Cheng; Wang, Kun-Te; Huang, Yueh‐Min

doi:10.1007/978-3-642-14484-4_2

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Determination of learning styles with certain methods and methods have been carried out in previous studies and provide good results, such as predictions of learning styles with Fuzzy Decision Trees (Kanninen, 2009;Hsu, Wang, & Huang, 2010;Latham et al, 2012;Crockett, Latham, & Whitton, 2017), one of which is by the forward chaining method. The forward chaining method is an inference multiplication that connects a problem with a solution called the chain (Harjanto et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Designing an expert system for determining student learning styles using forward chaining in engineering education

Anwar

2021

JKP

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Engineering education prepares its graduates able to work in the community and entrepreneurship, the quality of graduates is highly determined by the quality of the learning process is no exception for student learning styles. The purpose of this study was to describe the design of the expert system to determine the student learning style, the method used was forward chaining. The results of the design of this application have been able to diagnose student learning styles efficiently and provide the best solutions for their learning.

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Section: Introductionmentioning

confidence: 99%

Designing an expert system for determining student learning styles using forward chaining in engineering education

Anwar

2021

JKP

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“…FSLSM is a learning style model that describes learning styles in detail and is therefore highly appropriate for providing adaptively in learning systems. Furthermore, FSLSM is used very often in technology enhanced learning and some researchers even argue that it is the most appropriate learning style model for the use in adaptive learning systems such as [12], [13], [14] as well as, it is easy to implement. Furthermore the learning styles can be obtained by means of the Index of Learning Styles (ILS) instrument, a questionnaire based on 44 items to which each student responds according to their learning preferences [6] as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

Nafea

Siewe

2019

2019 International Conference on Innovative Trends in Computer Engineering (ITCE)

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Explosive growth of e-learning in the recent years has faced difficulty of locating appropriate learning resources to match the students learning styles. Recommender system is a promising technology in e-learning environments to present personalised offers and convey appropriate learning objects that match student inclinations. This paper, proposes a novel and effective recommender algorithm that recommends personalised learning objects based on the student learning styles. Various similarity metrics are considered in an experimental study to investigate the best similarity metrics to use in a recommender system for learning objects. The approach is based on the Felder and Silverman learning style model which is used to represent both the student learning styles and the learning object profiles. It was found that the K-means clustering algorithm, the cosine similarity metrics and the Pearson correlation coefficient are effective tools for implementing learning object recommender systems. The accuracy of the recommendations are measured using traditional evaluation metrics, namely the Mean Absolute Error and the Root Mean Squared Error.

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An Approach for Non-deterministic and Automatic Detection of Learning Styles with Deep Belief Net

Manga

Ndjodo

2021

Lecture Notes in Networks and Systems

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Modeling Personalized Learning Styles in a Web-Based Learning System

Cited by 8 publications

References 5 publications

Designing an expert system for determining student learning styles using forward chaining in engineering education

Designing an expert system for determining student learning styles using forward chaining in engineering education

A Novel Algorithm for Course Learning Object Recommendation Based on Student Learning Styles

An Approach for Non-deterministic and Automatic Detection of Learning Styles with Deep Belief Net

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