Development of a Framework for Predicting Students' Academic Performance in STEM Education using Machine Learning Methods

In the continuously evolving educational landscape, the prediction of students' academic performance in STEM (Science, Technology, Engineering, Mathematics) disciplines stands as a paramount component for educational stakeholders aiming at enhancing learning methodologies and outcomes. This research paper delves into a sophisticated analysis, employing Machine Learning (ML) algorithms to predict students' achievements, focusing explicitly on the multifaceted realm of STEM education. By harnessing a robust dataset drawn from diverse educational backgrounds, incorporating myriad factors such as historical academic data, socioeconomic demographics, and individual learning interactions, the study innovates by transcending traditional prediction parameters. The research meticulously evaluates several machine learning models, juxtaposing their efficacies through rigorous methodologies, including Random Forest, Support Vector Machines, and Neural Networks, subsequently advocating for an ensemble approach to bolster prediction accuracy. Critical insights reveal that customized learning pathways, preemptive identification of at-risk candidates, and the nuanced understanding of contributing influencers are significantly enhanced through the ML framework, offering a transformative lens for academic strategies. Furthermore, the paper confronts the ethical quandaries and challenges of data privacy emerging in the wake of advanced analytics in education, proposing a holistic guideline for stakeholders. This exploration not only underscores the potential of machine learning in revolutionizing predictive strategies in STEM education but also advocates for continuous model optimization, embracing a symbiotic integration between pedagogical methodologies and technological advancements, thereby redefining the trajectories of educational paradigms.