Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning

TL;DR

This paper employs an LSTM-based machine learning approach to analyze educational big data, effectively predicting student performance and supporting personalized education through capturing long-term learning trends.

Contribution

It introduces a deep learning model using LSTM for educational data mining, enhancing prediction accuracy of student performance over traditional methods.

Findings

LSTM outperforms other models in predicting student performance

Deep learning effectively captures long-term educational trends

Model validation confirms high accuracy and generalization

Abstract

The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results.