Deep Learning-based System for Predicting College Student's Performance in Labor Education

A deep learning-based system was proposed for predicting college students' labor education performance in this study. The system addresses the limitations of traditional evaluation methods by leveraging advanced machine learning techniques. Through data collection, preprocessing, and model selection including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, the system predicts students' performance accurately. Model evaluation results ensure the system's reliability, usability, and effectiveness on the user interface. This system contributes to educational technology by providing educators with a valuable tool for enhancing labor education programs.