Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

Graf, Sabine; Kinshuk, Kinshuk; Liu, Tzu-Chien

doi:10.1109/icalt.2008.84

Cited by 146 publications

(140 citation statements)

References 6 publications

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“…This paper proposes an advanced student modelling approach which identifies learning styles according to the FSLSM. Investigations are based on previous research work [10], where an automatic and static student modelling approach has been designed, implemented, and evaluated. The evaluation showed that the approach is suitable for identifying learning styles and therefore can be seen as the basis for the development of an advanced student modelling approach which combines automatic, static, dynamic, and global student modelling.…”

Section: Identifying Learning Stylesmentioning

confidence: 99%

Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

Graf

Kinshuk

2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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Section: Identifying Learning Stylesmentioning

confidence: 99%

Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

Graf

Kinshuk

2009

2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology

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129

189

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“…Montazeri and Ghorbani [13] proposed an Evolutionary Fuzzy Clustering (EFC) methodology with Genetic Algorithm (GA) for the recognition of learning styles of e-learners. The work of Graf et al [14] proposed a literature-based approach that automatically detects the learning styles in LMSs. Dung and Florea [15] tracked data of learners' behaviors and used simple mapping rule to infer learning styles against Felder-Silverman Learning Style Model.…”

Section: A Adaptation Systems To Learner's Learning Stylesmentioning

confidence: 99%

The Impact of Learning Styles on Learner's Performance in E-Learning Environment

Abdullah¹,

Daffa²,

Bashmail³

et al. 2015

ijacsa

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Abstract-due to growing popularity of E-Learning, personalization has emerged as important need. Differences of learners' abilities and their learning styles have affected the learning outcomes significantly. Meanwhile, with the development of E-Learning technologies, learners can be provided more effective learning environment to optimize their performance. The purpose of this study is to determine the impact of learning styles on learner's performance in e-learning environment, and use this learning style data to make recommendations for learners, instructors, and contents of online courses. Data analysis in this research represented by user performance gathered from an E-learning platform (Blackboard), where this user performance data is represented by actions performed by platform's users. A 10-fold cross validation was used to create and test the model, and the data was analyzed by the WEKA software. Classification accuracy, MAE, and the ROC area have been observed. The results show that the accuracy of classification by means of NBTree technique had the highest correct value at 69.697% and it could be applied to develop Felder Silverman's learning style while taking into consideration students' preference. Moreover, students' performance increased by more than 12%.

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“…x being uncoupled from any Learning Management System (LMS), and independent of students actions in a specific system, as it occurs in traditional approaches, as [16,21];…”

Section: Related Workmentioning

confidence: 99%

“…A diversity of approaches for automatic detection of LS have been proposed, such as [21,20,8]. In general, these traditional approaches use deterministic inference systems for detecting students LS through predefined behavioural patterns.…”

Section: Related Workmentioning

confidence: 99%

A New Approach to Discover Students Learning Styles in Adaptive Educational Systems

Dorça

Lima

Fernandes

2013

RBIE

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Personalization according to specific requirements of an individual student is one of the most important features in adaptive educational systems. Considering learning and how to improve a student's performance, these systems must know the way in which an individual student learns best. In this context, the current work outlines a new approach to automatically and dynamically discover students learning styles, considering its non-deterministic and non-stationary aspects, and taking into account that learning styles may change during the learning process in an unexpected and unpredictable way. Our approach is mainly based in genetic algorithms and reinforcement learning, and it has been tested through computer simulation of students. Promising results have been obtained through experiments. Some of them are presented in this paper.

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Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

Abstract: Abstract

Cited by 146 publications

References 6 publications

Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

The Impact of Learning Styles on Learner's Performance in E-Learning Environment

A New Approach to Discover Students Learning Styles in Adaptive Educational Systems

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