A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

Petchboonmee, Phanthipha; Phonak, Duangkamol; Tiantong, Monchai

doi:10.7763/ijiet.2015.v5.590

Cited by 8 publications

(8 citation statements)

References 7 publications

(6 reference statements)

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“…Besides, when dynamic model is used to detect students’ learning style, a ‘cold start’ problem exists inevitably [ 14 ], because (i) there is not enough information available to build users' profile [ 15 ] and (ii) the system is unable to infer anything for the new users. To tackle this issue, [ 10 , 16 ] proposed hybrid detection method that combines the static and dynamic methods. As an example, Liyanage et al [ 10 ] used the Felder–Silverman learning style model as the basis to predict the learning style during the early stage and followed by the Bayesian network to mine the learning behaviour pattern in order to detect the learning style more accurately.…”

Section: Introductionmentioning

confidence: 99%

“…As an example, Liyanage et al [ 10 ] used the Felder–Silverman learning style model as the basis to predict the learning style during the early stage and followed by the Bayesian network to mine the learning behaviour pattern in order to detect the learning style more accurately. In another approach, Petchboonmee et al [ 16 ] used J48 and naive Bayes algorithm to establish the decision tree for the learning style prediction model, which is based on the Kolb learning style model. Although hybrid detection methods have been successful, we argue that simply combining the two detection methods (static and dynamic) remains a major drawback as students are still required to fill out a lengthy questionnaire.…”

Section: Introductionmentioning

confidence: 99%

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Students' learning style detection using tree augmented naive Bayes

Li¹,

Rahman²

2018

R. Soc. open sci.

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Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students’ learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students’ learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Students' learning style detection using tree augmented naive Bayes

Li¹,

Rahman²

2018

R. Soc. open sci.

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“…This clearly demonstrates the need for alternative methods using today's advanced technologies to overcome current drawbacks. In this respect, when the related literature is examined, it is seen that the machine learning and data mining techniques are used in determining the learning style of the person (Maaliw & Ballera, 2017;Rajper, Shaikh, Shaikh, & Ali Mallah, 2016;Petchboonmee, Phonak, & Tiantong, 2015). In addition, each of the standard methods that model learning styles divides learning styles into certain categories and places individuals in these categories according to their scores.…”

Section: Another Example Of the Learning Style Scales Is The Group-emmentioning

confidence: 99%

“…Abdullah, Daffa, Bashmail, Alzahrani, & Sadik (2015) investigated the effect of learning styles of learners on the performance of e-learning environment and obtained the best accuracy value with NBTree algorithm in these analyzes performed using WEKA. Petchboonmee et al (2015) performed learning style classification and investigated the effectiveness of Kolb's classification of experimental learning style by using WEKA. When some general questions such as gender, educational level, and Kolb's 32-item experiential learning style classification questionnaire were used…”

Section: Determining the Learning Stylesmentioning

confidence: 99%

A Model Proposal to Determine Learning Styles of Students by Using Machine Learning Techniques and Kolb Learning Styles Inventory

Kartal¹,

Biber²,

Biber³

et al. 2019

Kastamonu Eğitim Dergisi

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Öz Öğrenme stillerini önceden belirlemek, öğrenme ortamının tasarımında, öğretim üyesinin ders içeriğini hazırlamasında ve özellikle öğrencinin öğrenme sürecinde önemli bir rol oynamaktadır. Kolb Öğrenme Stilleri Envanteri (KÖSE), öğrenme stillerini belirlemede en yaygın kullanılan araçlardan birisidir; ancak diğer araştırmalar, ölçekler veya psikolojik testlerde olduğu gibi KÖSE'nin de uygulama ve değerlendirme aşamalarında, soruların yanlış anlaşılması veya boş geçilmesi gibi bazı problemlerle karşılaşılabilir. Bu çalışmada; makine öğrenmesi teknikleri ve KÖSE Versiyon III (KÖSE-III) kullanılarak öğrencilerin öğrenme stillerini belirlemeye yönelik bir model önerisi geliştirmek ve bu modeli temel alan, web ve mobilden erişilebilen bir uygulama geliştirmek amaçlanmaktadır. Bu amaçla, KÖ-SE-III'te verilen durumlara yönelik Kolb'un orijinal değerlendirme yönteminden farklı olarak öğrencilerden kendilerine en uygun gelen seçeneği seçmeleri istenmiş ve öğrencilerin yaş ve cinsiyet bilgileri de alınarak araştırmanın veri seti oluşturulmuştur. Makine öğrenmesi tekniklerinden k-En Yakın Komşu Algoritması, C4.5 Karar Ağacı Algoritması ve Naive Bayes Sınıflandırıcısı kullanılarak en iyi performansı gösteren model seçilmiştir. Araştırma kapsamında geliştirilen uygulama e-öğrenme sistemlerine kolaylıkla entegre edilebileceğinden; öğreticilerin, öğrencilerin öğrenme stillerini belirleme süreçlerini kolaylaştırması, buna bağlı olarak eğitim etkinliklerinin öğrenci merkezli tasarlanmasına imkân tanıması ve daha çok öğrenciye ulaşılan bilimsel çalışmaların yapılabilmesi açısından bu çalışmanın önemli olduğu düşünülmektedir.

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“…and VARK model other learning style models available are Dun and Dun Model, MBTI Model, David Kolb"s Learning Style model etc. [34], [36]. Most of the studies are based on Felder and Silverman Learning Style Model which identifies the preferences over 8 learning styles grouped in 4 dimensions using ILS Questionnaire.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Dynamic eLearning Framework for Visual and Verbal Learners

Sethi¹,

Lomte²

2017

IJCSE

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In this paper, an eLearning Framework to identify visual and verbal learners is proposed. The proposed framework identifies learning style using ILS Questionnaire based on Felder Silverman Learning Style Model. The learning strategies and learning assessment strategies of visual and verbal learners are identified using the two tests developed based on VARK model. The learner profile data is analyzed using K-Means algorithm to create learning style, learning strategy and assessment strategy clusters. The style specific learning objects are developed and delivered to learners using rule based method and FP Growth algorithm. This framework is useful for teachers to understand the learner and provide them personalized learning environment for improving the learning efficiency.

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A Comparative Data Mining Technique for David Kolb's Experiential Learning Style Classification

Cited by 8 publications

References 7 publications

Students' learning style detection using tree augmented naive Bayes

Students' learning style detection using tree augmented naive Bayes

A Model Proposal to Determine Learning Styles of Students by Using Machine Learning Techniques and Kolb Learning Styles Inventory

An Interactive Dynamic eLearning Framework for Visual and Verbal Learners

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