The integration of big data into education has opened new possibilities for evaluating learning effectiveness. Traditional evaluation methods, such as exams and quizzes, often fail to capture the diverse factors that contribute to a student’s learning journey. This study explores the potential of big data analytics in creating more accurate and comprehensive evaluations of learning effectiveness. By analyzing data from various learning platforms, including student behaviors, engagement metrics, and performance, the study presents a framework that integrates machine learning techniques to evaluate student performance. The research highlights the value of predictive analytics for identifying students at risk of underperforming, and the potential for personalized learning interventions. Through a case study of an online learning platform, this study demonstrates how big data can provide actionable insights to improve educational outcomes. The paper also addresses the challenges of privacy and data security, emphasizing the need for ethical considerations in implementing big data solutions in education. Overall, the study concludes that big data analytics can significantly enhance the accuracy and depth of learning effectiveness evaluations, offering opportunities for more targeted interventions and improved student success.

Chen Liu. Research on learning effectiveness evaluation based on big data. Frontiers in Educational Research (2025) Vol. 8, Issue 2: 174-182. https://doi.org/10.25236/FER.2025.080225.

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