An Efficient Machine Learning Enabled Algorithm to Predict Student Performance in Higher Education

Understanding a student’s progress rate requires a strong understanding of student performance prediction. We are attempting to determine the student’s current situation and forecast his or her future outcomes in this research. Teachers are able to groom students and provide them with appropriate assistance after results are known. Higher education institutions’ main goal is to provide their students with high-quality education. To achieve the highest level of quality in the educational system, knowledge must be developed to predict student enrollment in particular courses, identify problems with traditional classroom teaching models, detect unfair testing practices used online, detect abnormal values in student result sheets, and predict student performance. The proposed study uses actual data to forecast the student’s academic progress using a range of machine learning (ML) methodologies. A comparison of ML methods on several evaluation metrics has also been published. The students will benefit from being able to monitor their academic progress and adjust their study schedule as necessary to improve their performance in the future. This study presents the development and implementation of an efficient machine learning-enabled algorithm tailored for predicting student outcomes in higher education institutions. The algorithm utilizes advanced techniques, including feature engineering, ensemble learning, and real-time monitoring, to analyze diverse datasets encompassing academic records, socio-economic factors, and behavioral indicators. The primary objectives include designing a predictive model with high accuracy and generalizability, integrating real-time monitoring for proactive interventions, and addressing ethical considerations such as bias mitigation and transparency.