Prediction Model of Student Achievement in Business Computer Disciplines - Pratya Nuankaew (International Journal of Emerging Technologies in Learning (iJET), 2020)
An educational program that does not accept the change of disruptive technology will inevitably result in future destruction. There are two objectives including (1) to construct reasonable students’ dropout prediction model for business computer disciplines, and (2) to evaluate the model performance. Data collected consists of 2,017 records from students who enrolled in the business computer program at the School of Information and Communication Technology, the University of Phayao. Research tools are divided into two parts. (1) Modelling; it consisted of the Artificial Neural Network Algorithm, Decision Tree Algorithm, and Naïve Bayes Algorithm. (2) Model testing; it consisted of the confusion matrix performance, accuracy, precision, and recall measurement. It is a clear innovation in the research that the researcher combines the knowledge of data science in analysis to improve the academic achievement of students in higher education in Thailand. From the analysis results, its show that the model developed from using Artificial Neural Network algorithms has the highest accuracy in the first three data sets (89.04%, 92.70% and 93.71%), and the last model is appropriate for Naïve Bayes algorithm (91.68%). Finally, it is necessary to conduct additional research and present research results to relevant parties and organizations.