Big Data-Driven Automated Engagement Quantification Model for Language Learning

To address the limitations in accuracy associated with conventional assessment methods for English learning engagement, this study proposes a novel big data-driven automatic computation model. The framework incorporates a systematic data preprocessing phase utilizing advanced big data analytics to ensure dataset integrity. A multidimensional online learning behavior analysis index system was established through entropy weight method and analytic hierarchy process (AHP) for precise indicator weight allocation. Particularly, we introduced a hierarchical clustering algorithm combined with Pearson correlation matrix analysis to categorize behavioral correlations into distinct feature groups. These grouped characteristics were subsequently integrated into an ensemble learning architecture to construct the final engagement quantification model. Experimental validation demonstrates that the proposed model achieves a 40% improvement in measurement accuracy compared with traditional approaches (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p&lt;0.01$</tex>), while reducing computational latency by an average of 12 minutes per assessment cycle. The model effectively resolves critical issues in learning behavior pattern recognition and metric weight optimization, significantly enhancing the precision and efficiency of college students' English learning engagement evaluation. This methodological advancement provides an effective analytical tool for educational big data applications, particularly in second language acquisition research..