Student Achievement Prediction Model based on TLBO-BP

Abstract

Today, educational prediction analysis has become an important tool for educational institutions to analyze students, and the performance of students in educational prediction analysis is a critical feedback. However, there are serious challenges in dealing with multifactor datasets to improve the convergence and accuracy of predicting student performance. Therefore, this article comprehensively analyzes machine learning technologies, analyzes the community activities of middle-term students, and explores the impact of community activities on middle-aged students' practical skills. This paper uses Random Forest and Pearson Correlation Coefficient to analyze the relevance of the impact of community activities on student hands-on performance and use Teaching and Learning Algorithms (TLBO) to optimize Back propagation (BP) neural networks applied to predict students' future trends. The results show that the TLBO-BP model can more accurately predict dynamic changes in student performance and predict high accuracy in simple patterns.