Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data

Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data

Cheng Zhang, Jinming Yang, Mingxuan Li, Meng Deng
Copyright: © 2024 |Pages: 20
DOI: 10.4018/IJGCMS.348052
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Abstract

In this study, simulation and big data analytics are combined with machine learning techniques, specifically K-means clustering, Apriori algorithm, and a stacked integrated learning model, to predict academic performance of college students with a high accuracy of 95.5%. By analyzing behavioral data from over 1,000 undergraduates, we correlate various behaviors with academic success, focusing on the use of libraries, self-study habits, and internet usage. Our findings highlight the benefits of using big data and simulation in educational strategies, promoting effective resource allocation and teaching enhancements. The study acknowledges limitations due to its regional focus and proposes future research directions to enhance model generalization and technological integration for broader application.
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Recent studies have expanded the applications of big data across various fields. Meng et al. (2023) analyze the influence of Internet celebrity propaganda on purchasing decisions using big data, highlighting its role in consumer behavior analysis. Wang et al. (2023) and Zhang et al. (2023) explore big data’s utility in enhancing enterprise information security management and optimizing supply chain performance, respectively, underscoring its effectiveness in risk assessment and logistical operations. Additionally, Gao et al. (2023) employ social media big data for global market demand forecasting, demonstrating its potential to refine marketing strategies and product development. These works collectively illustrate the transformative impact of big data in driving strategic decision-making and operational efficiencies across sectors.

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