Forecasting Graduation Schedule Model of Higher Education Learners using Feature Selection Techniques

Forecasting Graduation Schedule Model of Higher Education Learners using Feature Selection Techniques
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-4
Year of Publication : 2023
Author : Wongpanya S. Nuankaew, Tipparat Sittiwong, Sittichai Bussaman, Patchara Nasa-Ngium, Pratya Nuankaew
DOI : 10.14445/22315381/IJETT-V71I4P231

How to Cite?

Wongpanya S. Nuankaew, Tipparat Sittiwong, Sittichai Bussaman, Patchara Nasa-Ngium, Pratya Nuankaew, "Forecasting Graduation Schedule Model of Higher Education Learners using Feature Selection Techniques, " International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 354-358, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P231

Abstract
The biggest concern for learners in the 21st century is that graduation does not fit their educational plan. This research aims to study factors affecting students' academic achievement in the Faculty of Education during the COVID-19 crisis in Thailand. The data was on the academic achievement of 90 students from the Bachelor of Art Program in Educational Technology and Communications at the Faculty of Education, Naresuan University, Phitsanulok, Thailand. The research process was analyzed using data mining techniques, including CRISP-DM procedures, decision tree algorithm, forward selection analysis, cross-validation techniques, and confusion matrix performance. This research found that the course 001211 Fundamental English was the most significant subject for delayed graduation, where the developed model has a very high level of accuracy (89.00%). Researchers can use such a model to create effective planning strategies for preventing graduation failures.

Keywords
Academic achievement, Educational data mining, Students dropout, Student model.

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