Personalized Learning Strategies and Effect Evaluation Based on Education Big Data Analysis

Abstract

This study focuses on personalized learning strategies based on educational big data analysis. It delves into the conceptualization, design, and effi cacy assessment of these strategies. Educational big data analysis involves ensuring data quality, dealing with outliers and missing values, and mining key indicators for designing personalized strategies. The study explores three main personalized learning methods—content-based recommendation, collaborative fi ltering, and deep learning—and their advantages and limitations. The research emphasizes the importance of evaluating personalized learning strategies based on multidimensional indicators, including academic performance, learning satisfaction, and effi ciency.