Identification of Felder-Silverman Learning Styles with a Supervised Neural Network

Zataraín-Cabada, Ramón; Barrón-Estrada, M. L.; Angulo, Viridiana Ponce; García, Adán José; García, Catalina Beatriz García

doi:10.1007/978-3-642-14932-0_60

Cited by 5 publications

(2 citation statements)

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“…browsing pattern, time spent on a course, type of resources used, etc) and consequently inferring a corresponding learning style. The distribution of implicit modeling methods employed in the works surveyed are as follows: four works analyzed students' performance (Alkhuraiji et al 2011;Zatarain-Cabada et al 2009, 2010aGilbert and Han 1999); only one analyzed students' feedback (Carmona et al 2008); one did not specify the method used (Georgiou and Makry 2004); and twenty one analyzed students' behavior. We think that the widespread use of educational systems, such as Moodle 1 and custom educational systems, have facilitated the automatic detection of learning styles based on the analysis of students' behavior.…”

Section: User Model Informationmentioning

confidence: 99%

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Automatic detection of learning styles: state of the art

2014

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A learning style describes the attitudes and behaviors, which determine an individual's preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students' lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.

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Section: User Model Informationmentioning

confidence: 99%

“…(Stash et al 2006). Intelligent tutoring systems were used in 15 % of the works analyzed: OSCAR (Crockett et al 2011;Latham et al 2012) and Zamna (Zatarain-Cabada et al 2009, 2010a. In turn, 15 % used a learning management system (Bousbia et al 2010;Chang et al 2009;García et al 2007García et al , 2008Lo and Shu 2005).…”

Section: Educational Systemsmentioning

confidence: 99%

Automatic detection of learning styles: state of the art

2014

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Including Personality Traits, Inferred from Social Networks, in Building Next Generation of AEHS

Andaloussi

Capus

Berrada

2018

Innovations in Smart Cities and Applications

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Discovering Latent Relationships Among Learning Style Dimensions Using Association Rule Mining

Latha

Kirubakaran

Thangaiah

2014

Computational Intelligence in Data Mining - Volume 2

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Learning is the process of acquiring knowledge or skill. It is the process of filtering, storing and organizing information in our brains. Learners differ by the ways in which perceive, process and receive information. Based on the means of processing the information, learners are considered to possess their own style of learning. The learning process tends to be more effective if the knowledge is disseminated to the learners in their own learning styles. In order to disseminate knowledge in different ways, learners should be categorized based on their styles and they should be trained in an appropriate manner. This paper proposes a novel method to detect the relationships between different dimensions in Felder-Silverman learning style model using a data mining technique known as association rules, thereby providing a simpler way to disseminate the knowledge in the context of technology enhanced learning.

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Identification of Felder-Silverman Learning Styles with a Supervised Neural Network

Cited by 5 publications

References 6 publications

Automatic detection of learning styles: state of the art

Automatic detection of learning styles: state of the art

Including Personality Traits, Inferred from Social Networks, in Building Next Generation of AEHS

Discovering Latent Relationships Among Learning Style Dimensions Using Association Rule Mining

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