Development of a Framework for Predicting Students Academic Performance in STEM Education using Machine Learning Methods
Abdrakhmanov R. Zhaxanova A. Karatayeva M. Niyazova G.Z. Berkimbayev K. Tuimebayev A.
2024Science and Information Organization
International Journal of Advanced Computer Science and Applications
2024#15Issue 138 - 46 pp.
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 atrisk 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.
classification , Load balancing , machine learning , server , software
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International University of Tourism and Hospitality, Turkistan, Kazakhstan
South Kazakhstan University named after M.Auezov, Shymkent, Kazakhstan
M. Auezov South Kazakhstan University, Shymkent, Kazakhstan
Khoja Akhmet Yassawi International Kazakh, Turkish University, Turkistan, Kazakhstan
Boston University, Boston, United States
International University of Tourism and Hospitality
South Kazakhstan University named after M.Auezov
M. Auezov South Kazakhstan University
Khoja Akhmet Yassawi International Kazakh
Boston University
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