Prediction Model of Student Achievement in Business Computer Disciplines

Authors

  • Pratya Nuankaew School of Information and Communication Technology, University of Phayao, Phayao, 56000, Thailand https://orcid.org/0000-0002-3297-4198
  • Wongpanya Nuankaew Faculty of Information Technology, Rajabhat Mahasarakham University, Maha Sarakham, 44000, Thailand
  • Direk Teeraputon Faculty of Education, Naresuan University, 65000, Thailand
  • Kanakarn Phanniphong Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-Ok, 20110, Thailand
  • Sittichai Bussaman Faculty of Science and Technology, Rajabhat Mahasarakham University, Maha Sarakham, 44000, Thailand

DOI:

https://doi.org/10.3991/ijet.v15i20.15273

Keywords:

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

Abstract


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.

Author Biographies

Pratya Nuankaew, School of Information and Communication Technology, University of Phayao, Phayao, 56000, Thailand

Pratya Nuankaew received a B.Ed. Degree in Educational Technology in 2001, M.Sc. degree in Information Technology in 2008 from Naresuan University, and a Ph.D. degree in Computer Engineering in 2018 from Mae Fah Luang University. He is currently a lecturer at the School of Information and Communication Technology, University of Phayao, Phayao, Thailand. His research interests are in online mentoring model, mentoring relationships, social network analysis, ubiquitous computing, learning analytics, digital education, and educational data mining.

Wongpanya Nuankaew, Faculty of Information Technology, Rajabhat Mahasarakham University, Maha Sarakham, 44000, Thailand

Wongpanya Nuankaew received a B.Sc. degree in Computer Science in 2004, and M.Sc. degree in Information Technology in 2007 from Naresuan University. She is currently a lecturer at the Faculty of Information Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand. Her research interests are in digital education, innovation and knowledge management, data science, and big data and information technology management.

Direk Teeraputon, Faculty of Education, Naresuan University, 65000, Thailand

Direk Teeraputon received his B.Ed. in Educational Technology in 1991 from the Silpakorn University, M.Ed. in Audio-Visual Education in 1995 and Ph.D. in Educational Technology and Communication in 2004 from the Chulalongkorn University. He is currently a Senior Lecturer at Faculty of Education, Naresuan University, Thailand. His research interests are instruction system design, learning resources center management, distance education, self-regulated learning and training.

Kanakarn Phanniphong, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-Ok, 20110, Thailand

Kanakarn Phanniphong received his B.B.A. degree in Information System in 2005 from Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand, M.B.A. degree Business Administration in 2008 and D.B.A. degree in Business in 2018 from Pathumthani University, Pathum Thani, Thailand. He is currently a lecturer at the Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-Ok, Bangkok, Thailand. His research interests are, innovation and knowledge management, and management information system.

Sittichai Bussaman, Faculty of Science and Technology, Rajabhat Mahasarakham University, Maha Sarakham, 44000, Thailand

Sittichai Bussaman received his B.Sc. in Statistic in 1990 from Srinakharinwirot University, M.Sc. in Computer Science and Information Technology in 1997 from King Mongkut's Institute of Technology Ladkrabang and Ph.D. in Educational Technology and Communications in 2013 from Mahasarakham University. He is currently an Associate Professor at the Faculty of Science and Technology, Rajabhat Maha Sarakham University, Maha Sarakham, Thailand. His research interests are data mining in education, online learning, pattern recognition, and Artificial Intelligence.

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Published

2020-10-19

How to Cite

Nuankaew, P., Nuankaew, W., Teeraputon, D., Phanniphong, K., & Bussaman, S. (2020). Prediction Model of Student Achievement in Business Computer Disciplines. International Journal of Emerging Technologies in Learning (iJET), 15(20), pp. 160–181. https://doi.org/10.3991/ijet.v15i20.15273

Issue

Vol. 15 No. 20 (2020)

Section

Papers