Research on Intelligent Analysis Algorithm for Student Learning Behavior Based on Multi-source Sensor Data Fusion

In the era of digital transformation, leveraging multi-source sensor data to analyze and enhance student learning behavior has become increasingly crucial. In this research, we propose a Learn-Sync Intelligent Fusion (LSIF) system that delves into Estimate Adam driven- Intelligent Gradient Boosting Machines (EA-IGBM) that integrate sensor data from diverse sources to provide extensive analysis for the student behavior based on engagement and performance using multi-source sensor data fusion. This research employs the sensor data for online and offline classes for predicting the student learning behaviors during the class. To provide a holistic view of student engagement and performance, these sensor data are normalized using min–max normalization. Recursive Feature Elimination (RFE) is employed to extract the normalized multi-source data to frame multiple features that are integrated using the feature-level fusion technique. The LSIF aggregates behavioral fusion data from online and offline activities, using the EA-IGBM algorithm to predict academic success and provide actionable feedback. This model stimulates using tensor flow 2.15. The proposed EA-IGBM algorithm for both offline and online learning engagement detection performance is more significant, which utilizes certain parameters like student engagement, interaction frequency, behavioral patterns, and distractive levels. The established system highlights the effectiveness of multi-source sensor data fusion in monitoring and optimizing student learning behavior.