Abstract—The goals of this study are to develop the
architecture of a system for predicting student performance
based on data science approaches (SPPS-DSA Architecture)
and evaluate the SPPS-DSA Architecture. The research process
is divided into two stages: 1) context analysis and 2)
development and assessment. The data is analyzed by means of
standardized deviations statistically. The research findings
suggested that the SPPS-DSA architecture, according to the
research findings, consists of three key components: i) data
source, ii) machine learning methods and attributes, and iii)
data science process. The SPPS-DSA architecture is rated as the
highest appropriate overall. Predicting student performance
helps educators and students improve their teaching and
learning processes. Predicting student performance using
various analytical methods is reviewed here. Most researchers
used CGPA and internal assessment as data sets. In terms of
prediction methods, classification is widely used in educational
data science. Researchers most commonly used neural networks
and decision trees to predict student performance under
Index Terms—Predicting student performance, data science, machine learning, SPPS-DSA architecture.
Kitsadaporn Jantakun and Thiti Jantakun are with the Department of Computer Education, Faculty of Education, Roi Et Rajabhat University, Roi Et, Thailand (e-mail: email@example.com, firstname.lastname@example.org).
Thada Jantakoon is with the Department of Information and Communication Technology for Education Department, Rajabhat Maha Sarakham University, Mahasarakham, Thailand (e-mail: email@example.com).
Cite: Kitsadaporn Jantakun, Thiti Jantakun, and Thada Jantakoon, "The Architecture of System for Predicting Student Performance Based on Data Science Approaches (SPPS-DSA Architecture)," International Journal of Information and Education Technology vol. 12, no. 8, pp. 778-785, 2022.Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).