Proposing a novel approach for classification and sequencing of Learning Objects in E-learning systems based on learning style

Anitha, D.; Deisy, C.

doi:10.3233/ifs-141312

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…First, the FSLSM is recognized as the most commonly used model for measuring individual learning styles on a general scale [73] in higher education [74] and has remained popular for many years across different disciplines in university settings and beyond. In the age of personalized instruction, this model has breathed new life into areas such as blended learning [75], online distance learning [76], courseware design [56], and intelligent tutoring systems [77,78]. Second, the FSLSM is based on previous learning style models; the FSLSM integrates all their advantages and is, thus, more comprehensive in delineating students' learning styles [79,80].…”

Section: Instrumentmentioning

confidence: 99%

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

Peng

Chen

et al. 2021

PLoS ONE

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Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students’ discipline-specific learning styles in a college blended learning setting.

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Section: Instrumentmentioning

confidence: 99%

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

Peng

Chen

et al. 2021

PLoS ONE

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“…Active students do not learn much in situations that require their passivity; rather, they work well in groups, tend to communicate with others, and enjoy operating [31–33]. Reflective students prefer to think about materials independently and progress by drawing on the experience of others [34,35].…”

Section: Learning Preferencesmentioning

confidence: 99%

The study of virtual reality adaptive learning method based on learning style model

Lin

Wang

Lan

2021

Comp Applic In Engineering

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Learning style is the endogenous cause of students' unique behaviors when they are performing learning tasks. The adaptive learning system that considers learning style can provide a personalized experience to stimulate students' enthusiasm, which had been widely studied in recent years. However, most of such existing systems are constructed based on a desktop environment, which leads to the less‐than‐ideal effect of personalized learning due to the limitation of interaction means and environmental dimension. Therefore, an adaptive virtual reality learning method based on the learning style model was proposed in this study. This method continuously iterated the identification of learning style based on students' subjective and objective data. Then, the content of virtual learning environment was sustainedly adjusted according to the identification results, thus enabling the environment to dynamically adapt to students' learning styles. To evaluate the feasibility of this proposed method, a controlled experiment on 152 participants was conducted. Results show that this method obtained relatively stable and accurate results of learning style identification, with an accuracy range of 74.38%–80.30%. Moreover, learning in the virtual environment constructed based on this method had a positive impact on students' learning motivation and outcomes.

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“…Out of the 102, 74 attempted to propose different methods of LO/LS suitability classification and recommendation through many different approaches, such as dynamic programming [Muhammad 2015], fuzzy logic classification [Anitha and Deisy 2015], data mining [Shuib et al 2014], the ones cited on the second paragraph of the section 4.2 among others. As expected the list is much bigger than the examples mentioned.…”

Section: Tab 4 Lms Usage Amountsmentioning

confidence: 99%

Learning Style identification and usage in academia: A systematic mapping

Almeida¹,

Souza²,

Barrére³

2019

Anais Do XXX Simpósio Brasileiro De Informática Na Educação (SBIE 2019)

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The different learning style models and applications might be an obstacle for those who are seeking to start to work in this field. Also, one might not be able to find some key papers that many regards as good starting points. This paper presents a systematic mapping, done with 102 papers, in regards to learning style identification and usage, and seeks to create a starting point for those who are starting to work on these research fields. The results show the predominance of a few learning style models and learning object metadata models. Also, there is a high amount of works focusing on personal solutions with non-automatic detection and adaptation models.

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Proposing a novel approach for classification and sequencing of Learning Objects in E-learning systems based on learning style

Cited by 12 publications

References 25 publications

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

The study of virtual reality adaptive learning method based on learning style model

Learning Style identification and usage in academia: A systematic mapping

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