Unified Model for Learning Style Recommendation

Authors

  • Unhawa Ninrutsirikun chool of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand https://orcid.org/0000-0001-9196-1613
  • Debajyoti Pal School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand https://orcid.org/0000-0002-7305-1015
  • Chonlameth Arpnikanondtand School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand https://orcid.org/0000-0001-8062-1947
  • Bunthit Watanapa School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand https://orcid.org/0000-0002-2086-3706

DOI:

https://doi.org/10.13052/jwe1540-9589.2058

Keywords:

Association Evaluation, Association Rules, Guideline, Learning Styles, Moderation Analysis, Style-fit Strategy

Abstract

Studying computer programming requires not only an understanding of theories and concepts but also coding adeptness. Success in studying or conducting such a course is definitely a challenge. This paper proposes a systematic learning style recommendation. The model is designed to evaluate students’ attributes and ongoing or formative learning outcomes for suggesting the effective style-fit strategy that facilitates learners to enhance their learning performances in terms of knowledge and skill. A two-stage association analysis was designed and conducted on a dataset collected from IT major students who enrolled in the Introduction to Computer Programming course. The first stage of association rules is to analyze and discover important relationships amongst learning styles, students’ attribute, and learning performance. The second stage of moderation analysis is then applied to probe the moderation effect of the different learning preferences on the relationship between student attributes and learning achievement. Experiments expose many insights, for example, mathematics and logical thinking are powerful assets of success in computer programming study. Association rules can effectively identify associations of learning styles and the learning performance in terms of knowledge or skills. By moderation analysis, students in the “Excellent” cluster have a broad learning style than other students. Two types of significant moderators, the universal and specific, exemplify how lecturers can flexibly post style-fit teaching strategies for a class-wide and specific group, respectively.

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Author Biographies

Unhawa Ninrutsirikun, chool of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand

Unhawa Ninrutsirikun is a lecturer at School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand. She will receive her Ph.D. in Information Technology from SIT, KMUTT, Thailand in 2021. Her research interests are in Computer and Education, Data Analysis, and Machine Learning.

Debajyoti Pal, School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand

Debajyoti Pal is a researcher at School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand. He received his Ph.D. in Information Technology from KMUTT in 2017. His research interests are in Quality evaluation of multimedia-services, IoT security and Human computer interaction.

Chonlameth Arpnikanondtand , School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand

Chonlameth Arpnikanondt received his Ph.D. degree from Georgia Tech in the US. He is currently an Assistant Professor in the School of Information Technology at KMUTT, Thailand. His research work has been in the application areas of systems and software design and modeling. His current research interests include mobile digital health, mobile-based digital transformation and usability.

Bunthit Watanapa, School of Information Technology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand

Bunthit Watanapa is an associate professor at School of InformationTechnology, King Mongkut’s University of Technology Thonburi Bangkok, Thailand. He has completed his D.Eng. in Industrial Engineering in 2004 from Asian Institute of Technology, Thailand. His research interests are in Artificial Neural Networks and Data Mining.

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Published

2021-07-19

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Communication, Multimedia and Learning Technology through Future Web Engineering