Machine Learning Models for Identifying at-Risk Students: Applications and Challenges in Higher Education
Abstract
Considering the fact that many higher education institutions experience massive dropout rates and serious academic underperformance, exploring predictive approaches for intervention has been a topic of concern for both researchers and educators alike. Nevertheless, based on machine learning models, it has proven possible to signal students who are in jeopardy of inadequate academic outcomes, providing the framework for timely, targeted and individualised intervention measures. These models analyse huge amounts of student data - academic performance, absences, socioeconomic status, behaviour, etc. - to predict the students most likely to sink behind or drop out. Institutions can then use supervised machine learning algorithms to classify students with high accuracy by risk levels. In fact, unsupervised learning techniques can be used to identify hidden patterns in student participation and learning behavior. Nonetheless, ML models for at-risk student identification face challenges, including data privacy Issues, ethical Issues, and the interpretability of the models. That said, the model is really only as good as the data it's trained on, and having high-quality data, as well as access to the data, is the primary deciding factor in how useful the model will be. Accurate predictions call for a significant amount of consistent, comparatively unbiased and complete data, but many ML based interventions depend on such data which increases the probability of error. There are also ethical concerns around the management of sensitive information pertaining to students and the potential stigmatization of students who have been identified as at-risk. Trust or accept students, faculty, and administrators for transparency, fairness, and explainability in model decision making. The other key challenge is the integration of these ML-based interventions. Targeted support mechanisms like mentoring, counseling or other forms of academic assistance are needed to ensure that predictions become results. ML-based solutions sound reasonable, and ML solutions are very scalable even inwards. Also, learning models need to be continuouslychanged and updated so that they remain accurate and relevant, as student behavior and learning environments are ever-evolving.