Predictive Model for Identification and Analysis of Factors Impacting Students Academic Performance Using Machine Learning Algorithms

Background: Education is a vital component of both societal and individual growth. To create effective learning environments, it is essential to understand the factors that influence student achievement. However, the application of machine learning algorithms can drive positive change, providing higher education institutions with effective solutions to their challenges. Aim: This research develops a predictive model to identify and analyze the factors affecting student academic performance, aiming to provide institutional administrators and lecturers with a better understanding of the key factors impacting student success. Method: A comprehensive dataset was collected from undergraduate students at Modibbo Adama University (MAU), Yola, comprising student-related, home-related, lecturer-related, and institution-related factors. Model development was carried out using Python in Google Colab, a cloud-based Jupyter notebook environment. Classification algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Method (GBM), and Random Forest (RF) were applied to predict student academic performance. Four evaluation metrics namely; accuracy, precision, recall, and f1-score were used to analyze the models. Results: The Random Forest model outperformed the other machine learning models, achieving an overall accuracy of 95%. For predicting low-performing students, the model achieved a precision of 0.9677, recall of 0.9474, and f1-score of 0.9574. For high-performing students, the precision was 0.9324, recall was 0.9583, and f1-score was 0.9452. These strong performance metrics across both low and high performing student groups demonstrate the effectiveness of the Random Forest model in accurately predicting student academic performance based on the factors considered in the study. Feature importance analysis identified lecture attendance, sponsor support, quality of facilities, and lecturer clarification as the most influential factors on student performance. Other features, such as accommodation, employment status, and program preference, were found to have a low impact. These findings emphasize the importance of considering a comprehensive set of student, lecturer, institution, and home-related factors to sustain a conducive learning environment and enhance educational practices.