Makine Öğrenmesi Tekniklerini ve Kolb Öğrenme Stilleri Envanterini Kullanarak Öğrencilerin Öğrenme Stillerinin Belirlenmesi için Bir Model Önerisi

Yıl 2019, Cilt: 27 Sayı: 5, 1875 - 1892, 15.09.2019

Elif Kartal , Sezer Köse Biber Mahir Biber Melodi Özyaprak İrfan Şimşek Tuncer Can

https://doi.org/10.24106/kefdergi.2863

Cited By: 1

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, KÖSE-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.

Anahtar Kelimeler

Kolb öğrenme stilleri envanteri, makine öğrenmesi, öğrenme stilleri

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

Öz

Determining the learning styles in advance plays an important role
in the design of the learning environment, in the preparation of the instructor’s
course content, and in the learning process of the learner in particular. Kolb’s
Learning Style Inventory (KLSI) is one of the most widely used tools to
determine learning styles. However, some problems such as misunderstood or unanswered
questions can be encountered in application and evaluation stages of the KLSI
as in the other questionnaires, scales or psychological tests. The aim of this
study is to develop a model proposal for determining learning styles of
students by using machine learning techniques and KLSI Version III (KLSI-III)
and based on this model to develop an application that can be accessible both
online and on mobile devices. For this purpose, data set of this research was
created by adding the age and gender attributes to the answers given as the
most appropriate option to KLSI-III (unlike Kolb’s original evaluation method).
Machine learning techniques such as k-Nearest Neighbor Algorithm, C4.5 Decision
Tree Algorithm and Naive Bayes Classifier were applied to this data set and the
model with the highest performance has been selected out of this data set. As
the application developed within the scope of this study can be easily
integrated into e-learning systems; it is thought that it is important for the
teachers to facilitate the process of determining the learning styles of the
students and accordingly to enable the student-centered design of the training
activities and the scientific studies reaching more students.

Anahtar Kelimeler

Kolb’s learning styles inventory, learning styles, machine learning techniques

Kaynakça

• Abdullah, M., Daffa, W. H., Bashmail, R. M., Alzahrani, M., & Sadik, M. (2015). The Impact of Learning Styles on Learner’s Perfor-mance in E-Learning Environment. International Journal of Advanced Computer Science and Applications, 6(9), 24–31. https://doi.org/10.14569/IJACSA.2015.060903 Acuna, E., & The CASTLE Research Group. (2015). dprep: Data Pre-Processing and Visualization Functions for Classification. Retrieved from https://CRAN.R-project.org/package=dprep Aşkar, P., & Akkoyunlu, B. (1993). Kolb öğrenme stili envanteri. Eğitim ve Bilim, 87, 37–47. Babadoğan, C. (2000). Öğretim stili odaklı ders tasarımı geliştirme. Milli Eğitim Dergisi, 147, 61–63. Balaban, M. E., & Kartal, E. (2018). Veri Madenciliği ve Makine Öğrenmesi Temel Algoritmaları ve R Dili ile Uygulamaları (Second Edition). Beyoğlu, İstanbul: Çağlayan Kitabevi. Bilgin, İ., & Durmuş, S. (2003). Öğrenme Stilleri ile Öğrenci Başarısı Arasındaki İlişki Üzerine Karşılaştırmalı Bir Araştirma. Educati-onal Sciences: Theory & Practice, 3(2), 383. Botsios, S., Georgiou, D., & Safouris, N. (2008). Contributions to adaptive educational hypermedia systems via on-line learning style estimation. Journal of Educational Technology & Society, 11(2). Brown, E., Cristea, A., Stewart, C., & Brailsford, T. (2005). Patterns in Authoring of Adaptive Educational Hypermedia: A Taxonomy of Learning Styles. Educational Technology & Society, 8(3), 77–90. Brown, H. D. (2000). Principles of Language Learning and Teaching (4th Edition). New York, USA: Addison Wesley Longman. Brownlee, J. (2016a). How To Get Better Machine Learning Performance. Retrieved March 11, 2018, from https://machinelearningmastery.com/machine-learning-performance-improvement-cheat-sheet/ Brownlee, J. (2016b). Supervised and Unsupervised Machine Learning Algorithms. Retrieved March 11, 2018, from https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/ Cassidy, S. (2004). An Overview of Theories, Models, and Measures. Educational Psychology, 24(4), 419–444. https://doi.org/10.1080/0144341042000228834 Chang, W. (2016). shinythemes: Themes for Shiny. Retrieved from https://CRAN.R-project.org/package=shinythemes Chang, W., Cheng, J., Allaire, J. J., Xie, Y., & McPherson, J. (2017). shiny: Web Application Framework for R. Retrieved from https://CRAN.R-project.org/package=shiny Choi, I. C., Kim, K. S., & Boo, J. (2003). Comparability of A Paper-Based Language Test and A Computer-Based Language Test. Language Testing, 20(3), 295–320. cran.r-project.org. (2018). The Comprehensive R Archive Network. Retrieved March 20, 2018, from https://cran.r-project.org/ Douglas, P. K., Harris, S., Yuille, A., & Cohen, M. S. (2011). Performance Comparison of Machine Learning Algorithms and Number of Independent Components Used in fMRI Decoding of Belief vs. Disbelief. NeuroImage, 56(2), 544–553. https://doi.org/10.1016/j.neuroimage.2010.11.002 Duff, A., & Duffy, T. (2002). Psychometric Properties of Honey & Mumford’s Learning Styles Questionnaire (LSQ). Personality and Individual Differences, 33(1), 147–163. https://doi.org/10.1016/S0191-8869(01)00141-6 Dunn, R., Beaudry, J. S., & Klavas, A. (1989). Survey of Research on Learning Styles. Educational Leadership, 46(6), 50–58. Dunn, R., Honigsfeld, A., Doolan, L. S., Bostrom, L., Russo, K., Schiering, M. S., … Tenedero, H. (2009). Impact of Learning-Style Instructional Strategies on Students’ Achievement and Attitudes: Perceptions of Educators in Diverse Institutions. The Clearing Hou-se: A Journal of Educational Strategies, Issues and Ideas, 82(3), 135–140. https://doi.org/10.3200/TCHS.82.3.135-140 Dunn, R. S., & Dunn, K. J. (1993). Teaching Secondary Students Through Their Individual Learning Styles: Practical Approaches for Grades 7-12. Boston, MA, USA: Pearson Education. Dunn, R. S., Dunn, K. J., & Price, G. E. (1989). Learning Style Inventory. Lawrence, KS, USA: Price Systems. Ekici, G. (2003). Öğrenme Stiline Dayalı Öğretim ve Biyoloji Dersi Öğretimine Yönelik Ders Planı Örnekleri. Ankara: Gazi Kitabevi. Emre, İ. E. (2017). Veri Madenciliği ile Çocukluk Çağındaki Akut Romatizmal Ateşin Kalp Hastalığına Etkilerinin Analizi (Yüksek Lisans Tezi). İstanbul Üniversitesi Fen Bilimleri Enstitüsü, İstanbul. Evin Gencel, İ. (2006). Öğrenme Stilleri, Deneyimsel Öğrenme Kuramına Dayalı Eğitim, Tutum ve Sosyal Bilgiler Program Hedefleri-ne Erişi Düzeyi (Doktora Tezi). Dokuz Eylül Üniversitesi, İzmir. Felder, R. M., Felder, G. N., & Dietz, E. J. (2002). The Effects of Personality Type on Engineering Student Performance and Attitudes. Journal of Engineering Education, 91(1), 3–17. https://doi.org/10.1002/j.2168-9830.2002.tb00667.x Felder, R. M., & Silverman, L. K. (1988). Learning and Teaching Styles in Engineering Education. Engineering Education, 78(7), 674–681. García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian Networks’ Precision for Detecting Students’ Lear-ning Styles. Computers & Education, 49(3), 794–808. https://doi.org/10.1016/j.compedu.2005.11.017 Garg, B. (2013). Design and Development of Naive Bayes Classifier (Master of Science Thesis). North Dakota State University, North Dakota. Ghazivakili, Z., Nia, R. N., Panahi, F., Karimi, M., Gholsorkhi, H., & Ahmadi, Z. (2014). The Role of Critical Thinking Skills and Learning Styles of University Students in Their Academic Performance. Journal of Advances in Medical Education & Professiona-lism, 2(3), 95–102. Gilakjani, A. P. (2012). Visual, Auditory, Kinaesthetic Learning Styles and Their Impacts on English Language Teaching. Journal of Studies in Education, 2(1), 104. https://doi.org/10.5296/jse.v2i1.1007 Gooden, D. J., Preziosi, R. C., & Barnes, F. B. (2009). An Examination of Kolb’s Learning Style Inventory. American Journal of Business Edution, 2(3), 57–62. https://doi.org/10.19030/ajbe.v2i3.4049 Gregorc, A. (1982). Gregorc Style Delineator: Development, Technical, And Administration Manual. Gabriel Systems, Inc.: Maynard, MA, USA. Griggs, S., & Dunn, R. (1996). Hispanic-American Students and Learning Styles. East Lansing, MI: National Center for Research on Teacher Learning. ERIC, Document Reproduction Service no. ED 393607. Grigorenko, E. L., & Sternberg, R. J. (1995). Thinking Styles. In D. H. Saklofske & M. Zeidner (Eds.), International Handbook of Personality and Intelligence (pp. 205–229). New York, USA: Plenum Press. Gülbahar, Y. (2005). Öğrenme Stilleri ve Teknoloji. Eğitim ve Bilim, 30(138), 10–17. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd ed.). San Francisco, CA, USA: Morgan Kaufmann Pub-lishers. Hein, T. L., & Budny, D. D. (1999). Teaching to Students’ Learning Styles: Approaches That Work (Vol. 2, pp. 12C1/7-12C114). Presented at the Frontiers in Education Conference, Stripes Publishing L.L.C. https://doi.org/10.1109/FIE.1999.841622 Helmy, Y., Abdo, A., & Abdallah, R. (2016). A Proposed Framework for Learning Style Prediction in Higher Education Environ-ments. International Journal of Advanced Research in Computer Science and Software Engineering, 6(3), 140–143. Hmedna, B., El Mezouary, A., Baz, O., & Mammass, D. (2017). Identifying And Tracking Learning Styles in MOOCs: A Neural Networks Approach. International Journal of Innovation and Applied Studies, 19(2), 267. Hornik, K., Buchta, C., & Zeileis, A. (2009). Open-Source Machine Learning: R Meets Weka. Computational Statistics, 24(2), 225–232. https://doi.org/10.1007/s00180-008-0119-7 Kagan, J., Rosman, B. L., Day, D., Albert, J., & Phillips, W. (1964). Information Processing in The Child: Significance of Analytic and Reflective Attitudes. Psychological Monographs: General and Applied, 78(1), 1–37. https://doi.org/10.1037/h0093830 Kahtz, A. W., & Kling, G. J. (1999). Field‐dependent and Field‐independent Conceptualisations of Various Instructional Methods with an Emphasis on CAI: a qualitative analysis. Educational Psychology, 19(4), 413–428. https://doi.org/10.1080/0144341990190403 Karasar, N. (2003). Bilimsel Araştırma Yöntemi: Kavramlar-İlkeler-Teknikler. Ankara: Nobel Yayın Dağıtım. Kartal, E., & Özen, Z. (2017). Dengesiz Veri Setlerinde Sınıflandırma. In O. Torkul, S. Gülseçen, Y. Uyaroğlu, G. Çağıl, & M. K. Uçar (Eds.), Mühendislikte Yapay Zeka ve Uygulamaları (Birinci, pp. 109–131). Sakarya: Sakarya Üniversitesi Kütüphanesi Yayınevi. Kaufmann, G. (1989). The Assimilator–Explorer Inventory. Bergen, Norway: University of Bergen, Department of General Psycho-logy, Cognitive Unit. Kharb, P., Samanta, P. P., Jindal, M., & Singh, V. (2013). The Learning Styles and The Preferred Teaching—Learning Strategies of First Year Medical Students. Journal of Clinical and Diagnostic Research: JCDR, 7(6), 1089. Kinshuk, Liu, T. C., & Graf, S. (2009). Coping with Mismatched Courses: Students’ Behaviour and Performance in Courses Mismatc-hed to Their Learning Styles. Educational Technology Research and Development, 57(6), 739–752. https://doi.org/10.1007/s11423-009-9116-y Kılıç, E. (2002). Baskın Öğrenme Stilinin Öğrenme Etkileri Tercihi ve Akademik Başarıya Etkisi. Eğitim Bilimleri ve Uygulama, 1(1), 1–15. Kolb, D. A. (1984). Experiential Learning. NJ, USA: Prentice-hall Englewood Cliffs. Kolb, D. A. (1985). Learning-Style Inventory: Self-Scoring Inventory and Interpretation Booklet: Revised Scoring. Boston: Mc Beer and Company. Kolb, D. A. (1999). The Kolb Learning Style Inventory, Version 3. Boston, MA, USA: Hay Resources Direct. Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249–268. Kuhn, M. (2017). caret: Classification and Regression Training. Retrieved from https://CRAN.R-project.org/package=caret Larrañaga, P. (2006). K-Nearest Neighbor. Retrieved from http://biocomp.cnb.csic.es/~coss/Docencia/ADAM/Sample/Sample_Classification.pdf Li, Y. S., Yu, W. P., Liu, C. F., Shieh, S. H., & Yang, B. H. (2014). An Exploratory Study of The Relationship Between Learning Styles and Academic Performance Among Students in Different Nursing Programs. Contemporary Nurse, 48(2), 229–239. Liyanage, M. P. P., Gunawardena, K. L., & Hirakawa, M. (2016). Detecting Learning Styles in Learning Management Systems Using Data Mining. Journal of Information Processing, 24(4), 740–749. https://doi.org/10.2197/ipsjjip.24.740 Logan, K., & Thomas, P. (2002). Learning Styles in Distance Education Students Learning to Program (pp. 29–44). Presented at the 14th Workshop of the Psychology of Programming Interest Group, Brunel University, London, UK. Retrieved from http://www.ppig.org/papers/14th-logan.pdf Lovelace, M. K. (2005). Meta-Analysis of Experimental Research Based on The Dunn and Dunn Model. The Journal of Educational Research, 98(3), 176–183. Lowy, A., & Hood, P. (2004). The Power of the 2 x 2 Matrix: Using 2 x 2 Thinking to Solve Business Problems and Make Better Deci-sions. San Francisco, CA, USA: John Wiley & Sons. Maaliw, R. R. I., & Ballera, M. A. (2017). Classification of Learning Styles in Virtual Learning Environment Using J48 Decision Tree. Presented at the 14th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2017). McKenna, L., Copnell, B., Butler, A. E., & Lau, R. (2018). Learning Style Preferences of Australian Accelerated Postgraduate Pre-Registration Nursing Students: A Cross-Sectional Survey. Nurse Education in Practice, 28, 280–284. https://doi.org/10.1016/j.nepr.2017.10.011 Metallidou, P., & Platsidou, M. (2008). Kolb’s Learning Style Inventory-1985: Validity Issues And Relations with Metacognitive Knowledge About Problem-Solving Strategies. Learning and Individual Differences, 18(1), 114–119. https://doi.org/10.1016/j.lindif.2007.11.001 Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2017). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. Retrieved from https://CRAN.R-project.org/package=e1071 Ming, C. S. (2004). Effects of Programmed Learning Sequences Versus Traditional Instruction on The Achievement And Attitudes of Bermudian Seventh Graders in Social Studies And The Comparison of Two Learning-Style Identification Instruments’ Interpretations (Doctoral Thesis). St. John’s University. Mitchell, T. M. (1997). Machine Learning (1st ed.). USA: The McGraw-Hill Companies, Inc. Moussa, N. (2014). The Importance of Learning Styles in Education. Institute for Learning Styles Journal, 1, 19–27. Mumford, A., & Honey, P. (1992). Questions and Answers on Learning Styles Questionnaire. Industrial and Commercial Training, 24(7). https://doi.org/10.1108/00197859210015426 Narlı, S., Aksoy, E., & Ercire, Y. E. (2014). Investigation of Prospective Elementary Mathematics Teachers’ Learning Styles and Rela-tionships Between Them Using Data Mining. International Journal of Educational Studies in Mathematics, 1(1), 37–57. Okur, M., & Bahar, H. H. (2010). Learning Styles of Primary Education Prospective Mathematics Teachers; States of Trait-Anxiety and Academic Success. Procedia-Social and Behavioral Sciences, 2(2), 3632–3637. https://doi.org/10.1016/j.sbspro.2010.03.565 Özen, Z. (2016). Kimlik Doğrulaması için Tuş Vuruş Dinamiklerine Dayalı Bir Güvenlik Sisteminin Yapay Sinir Ağları ile Geliştirilme-si (Doktora Tezi). İstanbul Üniversitesi Fen Bilimleri Enstitüsü, İstanbul. Pantho, O., & Tiantong, M. (2016). Using Decision Tree C4. 5 Algorithm to Predict VARK Learning Styles. International Journal of the Computer, the Internet and Management, 24(2), 58–63. Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning Styles Concepts and Evidence. Psychological Science in The Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x Peker, M. (2003). Kolb Öğrenme Stili Modeli. Milli Eğitim Dergisi, 157, 185–192. Petchboonmee, P., Phonak, D., & Tiantong, M. (2015). A Comparative Data Mining Technique for David Kolb’s Experiential Learning Style Classification. International Journal of Information and Education Technology, 5(9), 672–675. https://doi.org/10.7763/IJIET.2015.V5.590 Peterson, E. R., Deary, I. J., & Austin, E. J. (2003). The Reliability of Riding’s Cognitive Style Analysis Test. Personality and Indivi-dual Differences, 34(5), 881–891. https://doi.org/10.1016/S0191-8869(02)00116-2 Pomplun, M., Frey, S., & Becker, D. F. (2002). The Score Equivalence of Paper-and-Pencil and Computerized Versions of A Speeded Test of Reading Comprehension. Educational and Psychological Measurement, 62(2), 337–354. Poon Teng Fatt, J. (2000). Understanding The Learning Styles of Students: Implications for Educators. International Journal of Socio-logy and Social Policy, 20(11/12), 31–45. Rajper, S., Shaikh, N. A., Shaikh, Z. A., & Ali Mallah, G. (2016). Automatic Detection of Learning Styles on Learning Management Systems using Data Mining Technique. Indian Journal of Science and Technology, 9(15), 1–5. https://doi.org/10.17485/ijst/2016/v9i15/85959 Raut, S. (2017). 5 Ways to Improve The Model Accuracy of Machine Learning. Retrieved March 11, 2018, from https://www.datasciencecentral.com/profiles/blogs/5-ways-to-improve-the-model-accuracy-of-machine-learning Riding, R. (2002). The Nature and Effects of Cognitive Style. In R. J. Sternberg & L. F. Zhang (Eds.), Perspectives on thinking, lear-ning, and cognitive styles (pp. 47–72). NJ, USA: Lawrence Erlbaum Associates. Riding, R. J. (1991). Cognitive Styles Analysis - CSA Administration. Birmingham: Learning and Training Technology. Riding, R., & Rayner, S. (2013). Cognitive Styles and Learning Strategies: Understanding Style Differences in Learning and Behavior. New York, NY, USA: Routledge. RStudio. (2017a). Shiny. Retrieved March 20, 2018, from https://shiny.rstudio.com/ RStudio. (2017b). shinyapps.io. Retrieved March 20, 2018, from https://www.shinyapps.io/ RStudio. (2018). RStudio – Open Source and Enterprise-Ready Professional Software for R. Retrieved March 20, 2018, from https://www.rstudio.com/ Samancı, N. K., & Keskin, M. Ö. (2007). Felder ve Soloman Öğrenme Stili İndeksi: Türkçeye Uyarlanması ve Geçerlik-Güvenirlik Çalışması. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi, 8(2). Sayad, S. (2017). Naive Bayesian. Retrieved May 27, 2017, from http://www.saedsayad.com/naive_bayesian.htm Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5(4), 13–22. Simpson, C., & Du, Y. (2004). Effects of Learning Styles and Class Participation on Students’ Enjoyment Level in Distributed Lear-ning Environments. Journal of Education for Library and Information Science, 45(2), 123–136. https://doi.org/10.2307/40323899 Şimşek, Ö. (2007). Marmara Öğrenme Stilleri Ölçeği’nin Geliştirilmesi ve 9-11 Yaş Çocuklarının Öğrenme Stillerinin İncelenmesi (Doktora Tezi). Marmara Üniversitesi. Eğitim Bilimleri Enstitüsü, İstanbul. Tanabe, H. (2006). Effects of Teaching Styles on Motivation for Self-Training by Students in Teaching of Presentation in L2. Academic Reports, Tokyo Polytechnic University, 28(2), 1–7. Truong, H. M. (2015). Integrating Learning Styles into Adaptive E-Learning System. In O. C. Santos, J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, … M. Desmarais (Eds.) (pp. 645–647). Presented at the 8th International Conference on Educa-tional Data Mining, Madrid, Spain. Retrieved from http://www.educationaldatamining.org/EDM2015/proceedings/edm2015_proceedings.pdf Uysal, M., Balbal, K. F., Mülayim, N., Özdemir, A., & Albeyoğlu, A. (2016). A Learning Style Inference System Based on Fuzzy Logic Technique. In The Eurasia Proceedings of Educational & Social Sciences (Vol. 4, pp. 590–597). Uysal, M. P. (2010). Öğrenme Stillerinin Bulanık Mantıkla Modellenmesi. In 4th International Computer & Instructional Technologies Symposium (pp. 1040–1045). Selçuk University Konya, Turkey. Vermunt, J. D., & Vermetten, Y. J. (2004). Patterns in Student Learning: Relationships Between Learning Strategies, Conceptions of Learning, and Learning Orientations. Educational Psychology Review, 16(4), 359–384. Veznedaroğlu, R. L., & Özgür, A. O. (2005). Öğrenme Stilleri: Tanımlamalar, Modeller ve İşlevleri. İlköğretim Online, 4(2). Visser, S., McChlery, S., & Vreken, N. (2006). Teaching Styles Versus Learning Styles in The Accounting Sciences in The United Kingdom and South Africa: A Comparative Analysis. Meditari Accountancy Research, 14(2), 97–112. https://doi.org/10.1108/10222529200600015 Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1–29. Williams, N., Zander, S., & Armitage, G. (2006). A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification. ACM SIGCOMM Computer Communication Review, 36(5), 5–16. Witkin, H. A., Oltman, P. F., Raskin, E., & Karp, S. A. (1971). Children’s Embedded Figures Test. Palo Alto, CA: Consulting Psyc-hologist Press, Inc. Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.). Cambridge, MA, USA: Morgan Kaufmann. Xu, Z., & Shi, Y. (2018). Application of Constructivist Theory in Flipped Classroom—Take College English Teaching as A Case Study. Theory and Practice in Language Studies, 8(7), 880–887. Yang, J., Huang, Z. X., Gao, Y. X., & Liu, H. T. (2014). Dynamic Learning Style Prediction Method Based on a Pattern Recognition Technique. IEEE Transactions on Learning Technologies, 7(2), 165–177. https://doi.org/10.1109/TLT.2014.2307858 Zajacova, B. (2013). Learning Styles: An Overview of Concepts and Research Tools and Introduction of Our Research Design in Phy-sics Education Field. In WDS’13 Proceedings of Contributed Papers (Vol. Part III, pp. 91–96). Zhang, L.F., Sternberg, R. J., & Rayner, S. (Eds.). (2011). Handbook of İntellectual Styles: Preferences in Cognition, Learning, and Thinking. New York, NY, USA: Springer Publishing Company.