Prediction Model of Student Achievement in Business Computer Disciplines

Pratya Nuankaew, Wongpanya Nuankaew, Direk Teeraputon, Kanakarn Phanniphong, Sittichai Bussaman


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.


Prediction Model; Students Academic Achievement; Educational Data Mining; Learning Analytics

Full Text:


Copyright (c) 2020 Pratya Nuankaew, Wongpanya Nuankaew, Direk Teeraputon, Sittichai Bussaman

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo