PROFILING LEARNING PREFERENCES OF DISTANCE EDUCATION STUDENTS BASED ON NEURAL NETWORK ANALYSIS

Year 2022, Volume: 23 Issue: 2, 1 - 14, 30.03.2022

Mehmet Fırat

https://doi.org/10.17718/tojde.1095592

Abstract

The learning preferences of the learners are of prime importance in the planning of distance education systems and the design of learning environments. Learning technique and learning material preferences are considered as the two most common and referable learning preferences to understand the learning preferences profile of distance education students. This research investigates the learning preferences of distance education students. Data was collected from 3390 distance education students from Anadolu University, considered as one of the mega universities in the world. Neurol Network Analyze conducted to profile learning preferences of distance education students. For this purpose, Multilayer Perception Model was applied as an artificial neural network analysis model in the analysis of data. The age of students was found as the most important independent variable on the prediction of material preferences and learning technique preferences of distance education students. The full Multilayer Perception Model of the learning preferences profile of distance education students was provided as a conclusion. Recommendations provided for future research and applications.

Keywords

Distance education, learning preferences, artificial neural network

References

• Alkhasawneh, R., & Hobson, R. (2011). Modeling student retention in science and engineering disciplines using neural networks. In 2011 IEEE Global Engineering Education Conference (EDUCON), 660-663.
• Altinpulluk, H., Kilinc, H., & Firat, M. (2020). Examination of Lifelong Learners’ Preferences for Learning Materials and Methods within the Context of Various Demographic Characteristics. European Journal of Teaching and Education, 2(1), 148-154. https://doi.org/10.33422/ejte.v2i1.184
• AOF, (2017). Anadolu Universitesi Acikogretim Sistemi 2017 Mezun Izleme Raporu. Anadolu Universitesi Yayin No: 3694.
• Costa, R. D., Souza, G. F., Valentim, R. A., & Castro, T. B. (2020). The theory of learning styles applied to distance learning. Cognitive Systems Research, 64, 134-145. https://doi.org/10.1016/j.cogsys.2020.08.004
• Firat, M. (2021). Uygulamadan Kurama Acik ve Uzaktan Ogrenme. Genisletilmis 2. Baski. Ankara: Nobel Akademi Yayinlari.
• Genovese, J. E. C. (2004). The Index of Learning Styles: An investigation of its reliability and concurrent validity with preference test. Individual Differences Research, 2(3), 169-174.
• Gilakjani, A. P. (2012). Visual, auditory, kinaesthetic learning styles and their impact on English language teaching. Journal of Studies in Education, 2(1), 104-113.
• Heidari, E., Sobati, M.A., Movahedirad, S. (2016). Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemom Intell Lab Syst 155:73–85. https://doi.org/10.1016/j.chemolab.2016.03.031
• IBM, (2021). Independent Variable Importance. Retrieved from https://www.ibm.com/docs/en/spss-statistics/26.0.0?topic=overtraining-independent-variable-importance on 28 October 2021.
• Ilin, V. (2021). The role of user preferences in engagement with online learning. E-Learning and Digital Media, 19(2), 189-208. https://doi.org/10.1177/20427530211035514
• Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1-11. https://doi.org/10.1016/j.compedu.2013.01.015