Development of predictive model for students’ final grades using machine learning techniques - Rahman, Nurul Habibah Abdul; Sulaiman, Sahimel Azwal; Ramli, Nor Azuana (AIP conference proceedings, 2024)
Predictive analytics is a new frontier sector of higher education in today's world of data science, similar to other businesses such as marketing, financial, fraud detection, and demographic trends. Predictive analytics can provide beneficial information to educators and potentially assist them in enhancing students' performance by analyzing historical data using a variety of approaches from data mining and machine learning. The e-learning practiced in today's education system are unfortunately cause the dropout rates among students. Dropouts may cause the big and negative issues for university system and the stakeholders as well. Based on the literature review, studies on machine learning and predictive analytics to improve student performance are still scarce in Malaysian higher education. Therefore, the objective of this quantitative research is to develop the best predictive model for predicting students' performance at Pahang Islamic University College using machine learning techniques such as Decision Tree, Random Forest, AdaBoost, and Gradient Boosting. Students who have taken the Business Statistics course from the years 2013 to 2021 will be the subjects of the study. Data retrieved through a Learning Management System were used. From the analysis that has been done, Random Forest is the best method to be used in the predictive model for students’ final grades.