Association of learning styles with different e-learning problems: a systematic review and classification

Khamparia, Aditya; Pandey, Babita

doi:10.1007/s10639-019-10028-y

Cited by 59 publications

(29 citation statements)

References 88 publications

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“…One of the most important individual differences that directly influence students' learning experiences is their learning styles. Learning style is defined as a method that a learner prefers and prioritizes in the learning process (Khamparia & Pandey, 2020 ; Kolb, 1984 ). For example, an individual learner might prefer and learn a topic better by hearing a lecture than by reading the same concept.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulations to Teach Science Subjects: Connections Among Students’ Engagement, Self-Confidence, Satisfaction, and Learning Styles

Almasri

2022

Educ Inf Technol

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With the increasing technology integration practices in education, the adoption of computer simulations to teach conceptual understanding of science concepts is widely accepted by educators across the globe. To understand the connections between learners’ engagement and satisfaction with simulations for science learning and their learning styles, the present study analyzed 1034 university students' perceptions and experiences of using simulations for learning physics, chemistry, and biology subjects. The study took place in a large public university in a gulf country. Precisely, this study provides an empirically driven exploration of the connection between tertiary students' engagement and satisfaction with simulation-based learning and their learning styles. The findings of this study showed that the participants showed a very high level of engagement and satisfaction with the use of simulations for learning science concepts in the subjects of physics, chemistry, and biology. Their self-confidence and VAK learning styles, particularly the kinesthetic style, were significant predictors of their engagement and satisfaction with the learning process. The findings from this study have implications for the benefit of researchers and practitioners interested in the effective adoption of computer simulations as a pedagogical approach in science education.

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Section: Literature Reviewmentioning

confidence: 99%

Simulations to Teach Science Subjects: Connections Among Students’ Engagement, Self-Confidence, Satisfaction, and Learning Styles

Almasri

2022

Educ Inf Technol

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“…The identification of learning style provides a basis for the adaptation of the virtual environment. Some desktop‐based adaptive systems adjust attributes of learning objects to adapt to students, such as types, sequence in navigation, and complexity [44–46]. In this setup, the degree of personalization of the learning experience is difficult to improve.…”

Section: Construction Of Adaptive Virtual Environmentmentioning

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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“…Further, considering their preferences, skills, and needs, Khamparia and Pandey [23] delivered a dynamic learning material to learners. Eight different learning attributes of student learning were classified by Khamparia and Pandey [23] on National Centre for Biotechnical Information (NCBI) e-learning database: student prior knowledge, personality, learning style, cognitive style, grades from previous semester, anxiety, motivation, and study level. A recent systematic review and meta-analysis on 129 articles have shown the effectiveness of adaptive and dynamic learning merged with different learning problems and styles [23].…”

Section: Related Workmentioning

confidence: 99%

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

et al. 2020

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Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

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Association of learning styles with different e-learning problems: a systematic review and classification

Cited by 59 publications

References 88 publications

Simulations to Teach Science Subjects: Connections Among Students’ Engagement, Self-Confidence, Satisfaction, and Learning Styles

Simulations to Teach Science Subjects: Connections Among Students’ Engagement, Self-Confidence, Satisfaction, and Learning Styles

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

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

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