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IJIET 2022 Vol.12(8): 778-785 ISSN: 2010-3689
doi: 10.18178/ijiet.2022.12.8.1684

The Architecture of System for Predicting Student Performance Based on Data Science Approaches (SPPS-DSA Architecture)

Kitsadaporn Jantakun, Thiti Jantakun, and Thada Jantakoon

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 classification techniques.

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: jansri.kp@gmail.com, thiti100@gmail.com).
Thada Jantakoon is with the Department of Information and Communication Technology for Education Department, Rajabhat Maha Sarakham University, Mahasarakham, Thailand (e-mail: thada.phd@gmail.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).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Jon-Chao Hong
  • Managing Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2023: 2.8), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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