Automatic Detection of Learning Styles on Learning Management Systems using Data Mining Technique

Rajper, Samina; Shaikh, Noor Ahmed; Shaikh, Zubair A.; Mallah, Ghulam Ali

doi:10.17485/ijst/2016/v9i15/85959

Cited by 23 publications

(19 citation statements)

References 10 publications

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“…Learning Styles Inventory is understood as a four-dimensional cycle consisting of Concrete experience (CE), reflective observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE). Research on Kolbs' learning style focuses on behavior by using the concept of Questionnaire [34], [35], and log file [36]. Research conducted by [34] aims to detect learning styles using Kolb's 4-dimensional and 9-dimensional, while [35] to detect learner's learning styles in LMS automatically uses the Naive Bayesian technique to replace the Kolbs' Learning Styles Inventory (KLSI).…”

Section: Related Workmentioning

confidence: 99%

“…Research on Kolbs' learning style focuses on behavior by using the concept of Questionnaire [34], [35], and log file [36]. Research conducted by [34] aims to detect learning styles using Kolb's 4-dimensional and 9-dimensional, while [35] to detect learner's learning styles in LMS automatically uses the Naive Bayesian technique to replace the Kolbs' Learning Styles Inventory (KLSI). Research conducted by [36] aims to classify learner's learning styles based on the Decision Tree algorithm using the log data file.…”

Section: Related Workmentioning

confidence: 99%

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Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Hidayat¹,

Wardoyo²,

Sn³

et al. 2020

IJACSA

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Learning Management System (LMS) is well designed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student's learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student's learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Hidayat¹,

Wardoyo²,

Sn³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…(Chang et al, 2009) introduced a mechanism that uses k-nearest neighbor classification and genetic algorithms to classify and identify learning styles in a generic model. (Rajper et al, 2016) used Bayesian network to detect student's learning styles based on Kolb model. It used some attributes from student's behavior and data from a survey done.…”

Section: Related Workmentioning

confidence: 99%

“…A lot of research is made on this aspect. There are various learning style theories given by experts, but the Felder Silverman learning style theory is largely used by researchers on LMS for learning styles' identification on LMS (Rajper et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Using Data Mining Techniques on Moodle Data for Classification of Student’s Learning Styles

Kika¹,

Leka²,

Maxhelaku³

et al. 2019

Proceedings of the 47th International Academic Conference, Prague

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Building an adaptive e-learning system based on learning styles is a very challenging task. Two approaches to determine students learning style are mainly used: using questionnaires or data mining techniques on LMS log data. In order to build an adaptive Moodle LMS based on learning styles we aim to construct and use a mixed approach. 63 students from two courses that attended the same subject "User interface" completed the ILS (Index of Learning Styles) questionnaire based on Felder-Silverman model. This learning style model is used to assess preferences on four dimensions (active/reflective, sensing/intuitive, visual/verbal, and sequential/global). Moodle keeps detailed logs of all activities that students perform which can be used to predict the learning style for each dimension. In this paper we have analyzed student's log data from Moodle LMS using data mining techniques for classifying their learning styles focusing on one dimension of Felder-Silverman learning style: visual/verbal. Several classification algorithms provided by WEKA as J48 Decision Tree classifier, Naive Bayes and Part are compared. A 10-fold cross validation was used to evaluate the selected classifiers. The experiments showed that the Naive Bayes reached the best result at 71.18% accuracy.

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“…Nesse sentido, a utilização dos recursos de tecnologias de informação e comunicação nas perspectivas de aprendizagem tem se mostrado abundante em termos de vantagens, ainda que algumas limitações tenham sido reconhecidas, tais como a falta de supervisão e assistência do professor (Rajper, Shaikh, Shaikh, & Mallah, 2016). Mudanças têm sido notadas tanto na forma de aprender como na forma de ensinar, de maneira que tal temática deve ser cada vez mais investigada pelas diferentes ciências interessadas no processo de ensinoaprendizagem.…”

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Escala de Estilos de Aprendizagem em Situações de Uso de Tecnologias: busca por evidências de validade de conteúdo

Roza

Wechsler

Nakano

2018

Estud. Interdiscip. Psicol.

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ResumoEsse estudo teve como objetivo a busca por evidências de validade de conteúdo de uma nova escala, em desenvolvimento, intitulada Escala de Estilos de Aprendizagem em Situações de Uso de Tecnologias. Para tanto, quatro juízes na área de avaliação psicológica avaliaram os 40 itens que compõem a escala, considerando quatro estilos de aprendizagem: ativo, reflexivo, teórico e pragmático, estabelecidos por Alonso, Gallego e Honey (1997). Originalmente, a maioria dos itens (n=34) apresentou índices de concordância acima de 75%, mostrando-se satisfatórios. Duas novas rodadas de avaliação foram conduzidas com os itens que foram reformulados ou substituídos por novos, sendo que, após esse procedimento, atingiram o índice de concordância esperado. As análises dos coeficientes Kappa evidenciaram índices acima de 0,75. Os resultados apontaram a adequação da escala aos conteúdos do modelo que pretende avaliar, mostrando-se adequada para uso em outros estudos, empíricos, de investigação de suas qualidades psicométricas. Palavras-chave: Estilo de aprendizagem; Tecnologia; Tecnologia da informação; Avaliação psicológica; Validade do teste. LEARNING STYLES SCALE IN TECHNOLOGY USE SITUATIONS: SEARCH FOR CONTENT VALIDITY EVIDENCES AbstractThis study aimed to search for evidence of content validity of the Learning Styles Scale in Technology use situations, a scale in development. For that, four judges working in the psychological assessment evaluated the 40 items of the scale, considering four learning styles: active, reflective, theoretical, and pragmatic, proposed by Alonso, Gallego and Honey (1997). Originally, most of the items (n=34) showed concordance rates above 75%, being satisfactory. Two new rounds of evaluation were conducted with the items that have been rewritten or replaced by new ones, and after this procedure reached the expected level of agreement. The analyzes showed Kappa coefficients indices above 0.75. The results showed the adequacy of the scale to the contents of the model that it intends to evaluate, being suitable for use in other studies involving empirical research of its psychometric qualities. ProcedimentosA escala foi construída fazendo uso de termos comuns para usuários das tecnologias da informação e comunicação, tais como computador, tablet, smartphone, aplicativos (programas de computador), conteúdos digitais (apresentações, texto, som, imagem em formato digital), Internet, sites, páginas (da web), e-mail, mensagem eletrônica, grupo de discussão, redes sociais.

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Automatic Detection of Learning Styles on Learning Management Systems using Data Mining Technique

Cited by 23 publications

References 10 publications

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Using Data Mining Techniques on Moodle Data for Classification of Student’s Learning Styles

Escala de Estilos de Aprendizagem em Situações de Uso de Tecnologias: busca por evidências de validade de conteúdo

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