Abstract
Under the background of rapid development of information technology, educational informatization has gradually become a key factor to promote educational reform and improve teaching quality. As the frontier of the current technology field, big data and deep learning are increasingly applied in education to enable personalized learning and dynamic resource allocation, providing new opportunities for the construction and optimization of educational information platform. This study investigates the construction and application effect of educational information platform based on big data and deep learning technology. Through in-depth analysis of the teaching data of 200 schools across the country, it is found that deep learning shows significant advantages in students’ behavior pattern recognition, personalized learning path recommendation, and teaching quality evaluation. The personalized recommendation system improved the average score of the experimental group students by 17.8% (p < 0.05) compared to the control group. The accuracy of the teaching quality evaluation system based on attention mechanism reached 94.2%, which was significantly higher than the traditional rule-based method (baseline accuracy of 82%). In addition, the research also shows that through big data analysis, educational administrators can more accurately grasp the development trend of education and formulate more targeted teaching strategies. The system demonstrated a 17.8% improvement in student performance and 94.2% accuracy in teaching quality evaluation. However, limitations such as potential data imbalance among participating schools and model sensitivity to historical academic patterns may affect generalization. Future work will explore calibration techniques to mitigate these risks.
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