Investigation on Machine Learning Algorithms to Predict High School Students Performance at Risk of Failure

• Published in: 2022 International Conference on Data Analytics for Business and Industry (ICDABI) pp. 77 - 82
• Main Authors: Almehaiza, Fatema, Jaafar, Layla, Alatoom, Iyad Ali Mohammad, Hewahi, Nabil Mahmood
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.10.2022
• Subjects: Algorithms; Decision Tree; Education; Educational Data Mining; Logistic Regression; Machine Learning; Machine learning algorithms; Prediction algorithms; Predictive models; Radio frequency; Random Forest; Reproducibility of results; Sociology; students' performance
• DOI: 10.1109/ICDABI56818.2022.10041554

Successful learning is dependent on the success of the learners; ensuring students' achievement is the responsibility of the educational system. Over the years, educational institutions have utilized Machine Learning (ML) technology to facilitate and support decision-making in a variety of learning areas. Predicting students' performance in the early stages of their learning journey is one of ML's most significant contributions to educational institutions. Using ML technology, the institutes will be able to improve teaching and learning practices for students who are at risk of failing. The objective of this study is to build a model that can predict students' performance and identify students at risk of failure. The study examines the application of three ML algorithms in predicting: Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The study used a sample size of 647 with one dependent variable and 33 distinct variables. The study found that the two tree-based methods performed better than LR, although the precession measure out of four measures shows that LR is slightly better. Overall, in this study RF has showed to be the best among them with an accuracy of80.37%, recall of 87.98%, and F1 rate of 91.78%.