A Study on a Personalized Learning Intervention Model Based on Learning Behavior Data Analysis

This study focuses on introducing a learning model related to personalized intervention which takes advantage of learning behavior data analytics to enhance students' academic performance and engagement. The study utilizes the learning analytics dataset from the Open University, which consists of multidimensional variables including out-of-class study time, parental involvement and extracurricular activity participation. Through analyzing correlation and regression, the study identifies significant relevance of academic achievement to key factors like out-of-class study time and parental support. On the basis of these findings, the researchers develop a targeted intervention framework which includes various strategies to optimize study time, recommend personalized resources, and increase parental involvement. Results show that these interventions greatly help students manage study time and raise parents’ involvement in their study. The results also reveal that these interventions significantly improve students’ academic performance and enhance their motivation. This study highlights the superiority of data-driven approaches to strengthen personalized learning and offers valuable insights for educators when they design focused and evidence-based interventions. Future work may be centered on refining the model to integrate more intricate patterns of educational behavior data and expand its applicability in diverse educational settings.