Construction and application of college students' performance prediction model based on BP neural network

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 13416; pp. 134162S - 134162S-7
• Main Authors: Guo, Haitao, Dilan, Raymund
• Format: Conference Proceeding
• Language: English
• Published: SPIE 08.11.2024
• ISBN: 9781510686106; 151068610X
• ISSN: 0277-786X
• DOI: 10.1117/12.3049613

Under the background of the data age, the pace of digital transformation and upgrading of higher education has gradually accelerated, which has led to the rapid collection of educational big data, so that the limitations of traditional college students' performance management system in data processing and mining analysis have become increasingly prominent. In this regard, based on the actual application needs of colleges and universities, this paper will give full play to the efficiency, accuracy and adaptive advantages of artificial intelligence technology, and propose and construct a college students' performance prediction model based on BP neural network, in order to make up for the functional defects of the traditional college students' performance management system. Practice has proved that the model relies on the powerful nonlinear mapping ability of BP neural network, which can not only process the historical performance data of multiple students, multiple subjects and multiple semesters at the same time, but also integrate the attendance rate, completion rate, participation in extracurricular activities and other data as auxiliary features to improve the accuracy and scientificity of performance prediction, provide necessary support for realizing personalized teaching, optimizing the allocation of educational resources and strengthening the management of higher education, and help promote the highquality development of higher education.