Document Type : Original Research Paper


1 Strathmore University

2 Mzumbe University


This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found within the students learning environment. To obtain the learning styles, a data mining technique was used and this explicitly involved the use of pattern analysis in order to identify the underlying learning styles in the data collected from the learners. This paper highlights the five major learning styles that describe the patterns extracted from the collected data. Therefore, considering the changed learning ecosystem, it is clear that prediction of student learning styles can be done when the various factor inputs within the student environment are brought together and analyzed to focus on learning within internet-mediated environments.


Main Subjects

1.    Balavendran Joseph, R.D., et al., Intrinsic vs. Extrinsic Motivation in an Interactive Engineering Game. Journal of Advances in Computer Engineering and Technology, 2019. 5(1): p. 37-48; Available from:
2.    Gebre, E., A. Saroyan, and R. Bracewell, Students' engagement in technology rich classrooms and its relationship to professors' conceptions of effective teaching. British Journal of Educational Technology, 2014. 45(1): p. 83-96; Available from:
3.    Heid, M.K., Learning Important Mathematics From Contextualization and Networked Collaboration—A Review of The SimCalc Vision and Contributions: Democratizing Access to Important Mathematics. Journal for Research in Mathematics Education, 2015. 46(1): p. 125-129; Available from:
4.    Rutten, N., W.R. Van Joolingen, and J.T. Van Der Veen, The learning effects of computer simulations in science education. Computers & Education, 2012. 58(1): p. 136-153; Available from:
5.    Kiyici, F.B., The definitions and preferences of science teacher candidates concerning Web 2.0 tools: a phenomenological research study. TOJET: The Turkish Online Journal of Educational Technology, 2010. 9(2): p. 185-195; Available from:
6.    Selwyn, N., S. Gorard, and J. Furlong, Whose Internet is it anyway? Exploring adults’(non) use of the Internet in everyday life. European Journal of Communication, 2005. 20(1): p. 5-26; Available from:
7.    Hsieh, T.-C. and C. Yang, Do Online Learning Patterns Exhibit Regional and Demographic Differences? Turkish Online Journal of Educational Technology-TOJET, 2012. 11(1): p. 60-70; Available from:
8.    Vermunt, J.D. and V. Donche, A learning patterns perspective on student learning in higher education: state of the art and moving forward. Educational psychology review, 2017. 29(2): p. 269-299; Available from:
9.    Conijn, R., A. Van den Beemt, and P. Cuijpers, Predicting student performance in a blended MOOC. Journal of Computer Assisted Learning, 2018. 34(5): p. 615-628; Available from:
10.    Vermunt, J.D. and M.D. Endedijk, Patterns in teacher learning in different phases of the professional career. Learning and individual differences, 2011. 21(3): p. 294-302; Available from:
11.    Mayilvaganan, M. and D. Kalpanadevi. Comparison of classification techniques for predicting the performance of students academic environment. in 2014 International Conference on Communication and Network Technologies. 2014. IEEE.
12.    802.11-, I.C.S.L.M.S.C.J.A.I.S., Wireless LAN medium access control (MAC) and physical layer (PHY) specifications. 1999.
13.    Caeiro, M., M. Llamas, and L. Anido. E-learning patterns: an approach to facilitate the design of e-learning materials. in 7th IberoAmerican Congress on Computers in Education. 2004.
14.    Abdel-Aty, M., K.J.A.A. Haleem, and Prevention, Analyzing angle crashes at unsignalized intersections using machine learning techniques. 2011. 43(1): p. 461-470; Available from:
15.    Mutua, M.N., A Correlation study between learning styles and academic achievements among secondary school students in Kenya. 2015, University of Nairobi.
16.    Sunday, K., et al., Analyzing Student Performance in Programming Education Using Classification Techniques. International Journal of Emerging Technologies in Learning (iJET), 2020. 15(2): p. 127-144; Available from:
17.    Keshtkar Langaroudi, M. and M. Yamaghani, Sports result prediction based on machine learning and computational intelligence approaches: A survey. Journal of Advances in Computer Engineering and Technology, 2019. 5(1): p. 27-36; Available from:
18.    Ünal, F., Data Mining for Student Performance Prediction in Education, in Data Mining-Methods, Applications and Systems. 2020, IntechOpen.
19.    Natek, S. and M. Zwilling, Student data mining solution–knowledge management system related to higher education institutions. Expert systems with applications, 2014. 41(14): p. 6400-6407; Available from:
20.    Duin, R. and E. Pekalska, Pattern Recognition: Introduction and Terminology. 2016; Available from:
21.    Hughes, G. and C. Dobbins, The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and practice in technology enhanced learning, 2015. 10(1): p. 1-18; Available from:
22.    Acarali, D., et al., Survey of approaches and features for the identification of HTTP-based botnet traffic. Journal of Network and Computer Applications, 2016. 76: p. 1-15; Available from:
23.    Hussain, S., et al., Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 2018. 9(2): p. 447-459; Available from:'_Academic_Performance_Using_WEKA/links/5a548db1458515e7b7326bde/Educational-Data-Mining-and-Analysis-of-Students-Academic-Performance-Using-WEKA.pdf.
24.    Brownlee, J., How to perform feature selection with machine learning data in weka. Retrieved April, 2016. 1: p. 2018.