A Kohonen Network for Modeling Students’ Learning Styles in Web 2.0 Collaborative Learning Systems

Zataraín-Cabada, Ramón; Barrón-Estrada, M. L.; Zepeda-Sánchez, L.; Sandoval, Guillermo; Osorio-Velazquez, J. Moises; Urias-Barrientos, J. E.

doi:10.1007/978-3-642-05258-3_45

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…In effect some theories can be considered able to provide profound information about students as only the preference to learning as for example Multiple Intelligences [9]. Most of the previously mentioned proposals have used the Felder and Silverman learning styles [10], but recently the multiple intelligence theory [9] has been used, as can be seen in Declan et al [13], Cabada et al [2] and Matos et al [12].…”

Section: Adaptive Educational Systemsmentioning

confidence: 99%

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Development of Learning Styles and Multiple Intelligences through Particle Swarm Optimization

Moura¹,

Franco

Melo³

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Adaptivity is an important aspect in computer based educational environments and one where the building of automated and intelligent systems that support it has been a challenge. In fact, the automatic detection of the pedagogical traits in order to construct a student model that allows inference of the next step in the learning process has been one of the main goals when producing adaptive systems. Learning styles and multiple intelligence theories have been widely used in student modeling to show how a student adquires knowledge and highlights special learning abilities. Through the bringing together of these two areas of learning a picture of an individual student can be made. This information therefore becomes useful for a tutor to adapt the leraning process to the sudent. It is therefore within this context that this study proposes an innovative method that is driven by the Kolb learning process in order to improve the intelligence percentages by bringing out in each individual those areas in which they excel, but also help in the improvement of the learning process where deficiency is detected. Thus, this method is able to detect and correct automatically the initial sef-evaluation. The selection of learning objects during the learning process is carried out by a particle swarm algorithm.

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Section: Adaptive Educational Systemsmentioning

confidence: 99%

“…Cabada et al [2] and [3] propose a Kohonen network to adapt content according to learning styles using self-organizing maps. García et al [4] uses a bayesian network to model students and detect learning styles.…”

Section: Adaptive Educational Systemsmentioning

confidence: 99%

Development of Learning Styles and Multiple Intelligences through Particle Swarm Optimization

Moura¹,

Franco

Melo³

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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show abstract

“…e data interfaces were represented as cognitive systems, and strength and weaknesses of each student were also verified. Zatarain et al [31] suggested three Complexity different kinds of neural networks. In the presence of webbased environment, these approaches are used to detect the adaptive learning styles of the students.…”

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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“…Existem estudos que utilizam técnicas de aprendizagem supervisionada de máquina, tais como redes neurais e redes Bayesianas (Carmona e Castillo, 2008;Zatarain-Cabada et al, 2009). A dificuldade dessas abordagens é a elevada complexidade de implementação e grande consumo de recursos computacionais de forma que a presença simultânea de vários estudantes pode comprometer, ou mesmo inviabilizar seu uso.…”

Section: Estilos De Aprendizagem Como Alternativa Para Identificar O unclassified

Discussão sobre a Adaptabilidade em Ambientes U-learning baseada em Estilos de Aprendizagem

Seixas¹,

Filho²,

Gomes³

2016

Anais Do v Workshop De Desafios Da Computação Aplicada À Educação (DesafIE! 2016)

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This article aims to present and discuss with this community the adaptability process in the U-Learning environments. Such environments need to consider the identification of user needs, mobile technologies and the different learning situations that may occur. It is known that different environments make use of different recognition strategies adaptation process based on profiles. The literature emphasizes the limiting nature of this kind of recognition in these environments, mainly in and not consider the different learning styles.

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A Kohonen Network for Modeling Students’ Learning Styles in Web 2.0 Collaborative Learning Systems

Cited by 8 publications

References 8 publications

Development of Learning Styles and Multiple Intelligences through Particle Swarm Optimization

Development of Learning Styles and Multiple Intelligences through Particle Swarm Optimization

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

Discussão sobre a Adaptabilidade em Ambientes U-learning baseada em Estilos de Aprendizagem

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