The role of Big Data in enhancing education quality assessment methods - Chen, Lin; Zhang, Shuang; Abdullah, Mohd Na’Im (World Scientific, 2026)
The study investigated how Big Data technologies can enhance education quality assessment methods by examining the impact of data analytics and AI-driven approaches on the precision, effectiveness, and thoroughness of educational evaluations. Traditional assessment methods relying on subjective evaluations, small-scale surveys and delayed feedback mechanisms have been criticized for their inability to provide comprehensive, timely and impartial evaluations of educational quality. The research employed a mixed-methods approach combining systematic literature review with data analytics, utilizing machine learning techniques, natural language processing and predictive analytics to evaluate data from learning management systems, institutional databases and online learning platforms collected between January and December 2024. Educational institutions implementing Big Data-centric assessment methodologies demonstrated a 15–25% reduction in overall assessment errors, a 10% increase in student engagement, and a 7% decrease in course withdrawal rates, with one prominent public university achieving a 20% decrease in dropout rates among at-risk students through predictive modeling. The findings revealed that real-time data interpretation enabled timely intervention for learning difficulties, while predictive analytics and personalized learning pathways allowed educators to proactively address academic challenges, resulting in measurable improvements in retention rates and academic performance across multiple institutional benchmarks.