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

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.