Research on Student Performance Prediction Based on Deep Learning

Student performance serves as a crucial indicator for assessing learning outcomes across various educational institutions. Conducting reasonable predictions and analyses can provide insights into students' future academic achievements. Predicting student performance is a significant research direction in the current exploration of educational data analysis patterns. Accurate predictions can assist educational administrators and teachers in promptly identifying academic risks, optimizing the allocation of scarce educational resources, and consequently improving education quality and learning outcomes. This paper establishes a Convolutional Neural Network (CNN) prediction model, constructing training and testing datasets using PyTorch tensors based on existing student information data. By extracting target information columns, the model identifies patterns within the data and ultimately predicts a student's score in a specific subject for their next exam. Upon completing the predictions, the results are displayed in a chart alongside existing scores for analysis, providing a reference for developing subsequent training programs.