The application of improved backpropagation neural network in college student achievement prediction

Abstract: Educational institutions generate a large amount of digital data in their daily operations, which is stored in servers, forming a substantial educational data set. Extracting valuable information through practical data analysis has become a critical problem that needs to be solved urgently. Students' examination results are an essential basis for evaluating their learning status, which reflects the effect of school education to some extent. Therefore, we propose a model based on the BP network and Pandas to construct a prediction model for Pandas' performance in the first year and their successful graduation to explore the potential relationship between Pandas' performance in the freshman year and graduation, thus realizing the principle of early guidance and improvement of teaching quality. Through the random prediction experiment of 9,424 scores data of 304 students in 2017 and 2018 majoring in network engineering at a university, the accuracy rate is 96.71% after the experimental data analysis and verification, which has proved that there is a potential correlation between the students' first-year course scores and graduation. Meanwhile, the improved BP network proposed in the present research exhibits reasonable practicability and extensibility in the college student achievement prediction model.

How to cite this paper

 Qin, Q & Jiang, S. (2024). The application of improved backpropagation neural network in college student achievement prediction.Decision Science Letters , 13(3), 691-698.

Refrences

Aaron, S. (2023). Feature Data Dimensionality Reduction Algorithm for Online English Teaching Quality Evaluation Based on Self-organizing Feature Map Neural Network. Computer Informatization and Mechanical System, 3,10-18,doi:10.12250/JPCIAMS2023090502.
Ai, L., & Feng, T. (2022). Application of BP neural network in teaching quality evaluation of higher vocational education. Mobile Information Systems, 2022. doi:10.1155/2022/8644276.
Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J. M., & Raymundo, C. (2023, February). Artificial neural network model to predict student performance using nonpersonal information. In Frontiers in Education (Vol. 8, p. 1106679). Frontiers. doi:10.3389/FEDUC.2023.1106679.
Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J. M., & Raymundo, C. (2023, March). Corrigendum: Artificial neural network model to predict student performance using nonpersonal information. In Frontiers in Education (Vol. 8, p. 1171995). Frontiers. doi:10.3389/FEDUC.2023.1171995.
Chen, L., Wang, L., & Zhang, C. (2022). Teaching Quality Evaluation of Animal Science Specialty Based on IPSO-BP Neural Network Model. Computational Intelligence and Neuroscience,(2022),1-10, doi:10.1155/2022/3138885.
Dai, H., & Yang, X. (2022). Evaluation Model of Mathematics Teaching Quality Based on Recurrent Neural Network. Wireless Communications and Mobile Computing,(2022),1-9, doi:10.1155/2022/3539912.
Di, H., Zhang, H., & Li, P. (2023). Teaching Quality of Ideological and Political Education in Colleges Based on Deep Learning. International Journal of e-Collaboration (IJeC), 4(19),1-15, doi:10.4018/IJEC.316829.
Feng, W., & Feng, F. (2022). Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network. Computational Intelligence and Neuroscience,(2022),1-12, doi:10.1155/2022/7893792.
Francis Bindhia, K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of medical systems, 6(43),162, doi:10.1007/s10916-019-1295-4.
Gu, Q. (2022). Research on Teaching Quality Evaluation Model of Higher Education Teachers Based on BP Neural Network and Random Matrix. Mathematical Problems in Engineering, (2022),1-13, doi:10.1155/2022/5088853.
Kumar, M. G. V., Veena, N., Čepová, L., Raja, M. A. M., Balaram, A., & Elangovan, M. (2023). Evaluation of the Quality of Practical Teaching of Agricultural Higher Vocational Courses Based on BP Neural Network. Applied Sciences, 2(13),1180,doi:10.3390/APP13021180.
Lan, Q., & Fan, N. (2022). Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis. Journal of Environmental and Public Health, (2022),1-10, doi:10.1155/2022/1668750.
Li, L. J., Zhou, S. L., Chao, F., Chang, X., Yang, L., Yu, X., ... & Shen, Q. (2023). Model compression optimized neural network controller for nonlinear systems. Knowledge-Based Systems, 265, 110311. doi:10.1016/J.KNOSYS.2023.110311.
Li, X. (2022). A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network. Computational Intelligence and Neuroscience, (2022), 1-11, doi:10.1155/2022/6776603.
Liu, Y. (2022). Improved Convolutional Neural Networks for Course Teaching Quality Assessment. Advances in Multimedia, (2022),1-10,doi:10.1155/2022/4395307.
Mouti, S., Rihawi, S., Rasli, M. R., & Ariffin, S. (2023). Virtual Teaching Assistant for Capturing Facial and Pose Landmarks of the Students in the Classroom Using Deep Learning. International Journal of e-Collaboration (IJeC), 1. (19),1-18,doi:10.4018/IJEC.316663.
Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems With Applications, 4(41),1432-1462, doi:10.1016/j.eswa.2013.08.042.
Qi, X., Tan, A., & Gao, Y. (2022). Higher Education Teaching Quality Evaluation Model Based on Improved RBF Neural Network. Wireless Communications and Mobile Computing, 2022, 1-11, doi:10.1155/2022/5495728.
Qin, Q. (2020a). Research and Practice of Applied Undergraduate Teaching Quality Assurance System. International Journal of Computational and Engineering, 2022, 4-9, doi:10.2991/978-94-6463-044-2_2.
Qin, Q. (2020b). Research on Practice Teaching System of Micro-enterprise Training Center for Electronic Information Specialty. DEStech Transactions on Social Science Education and Human Science. doi:10.12783/DTSSEHS/ECEMI2020/34660.
Roshani, A. (2019). Students' performance prediction using hybrid classifier technique in incremental learning. IJBIDM, 2, 173-189,doi:10.1504/ijbidm.2019.101255.
Shi, Y. (2022). Application of Artificial Neural Network in College-Level Music Teaching Quality Evaluation. Wireless Communications and Mobile Computing, 2022, 1-9, doi:10.1155/2022/7370015.
Sun, X., & Zhang, Y. (2022). An Improved BP Neural Network Algorithm for the Evaluation System of Innovation and Entrepreneurship Education in Colleges and Universities. Mobile Information Systems, 2022, 1-10, doi:10.1155/2022/1007538.
Xu, F., & Xia, Y. (2022). Music Art Teaching Quality Evaluation System Based on Convolutional Neural Network. Computational and Mathematical Methods in Medicine, 2023, 1-9, doi:10.1155/2022/8479940.
Yi, C., Zhou, J., Gao, Q., Gao, J., & Zhang, W. (2023). MDNN: Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning. Computer Modeling in Engineering & Sciences, 1(136), 381-401,doi:10.32604/CMES.2023.023234.
Zhang, W., & Wen, Y. (2022). A Multivariate Evaluation Model of Physical Education Teaching Quality with Random Matrix Optimization Neural Network. Mathematical Problems in Engineering, 2022,1-11, doi:10.1155/2022/6553012.
Zhang, Z. (2022). Evaluation of Physical Education Teaching Quality Based on the Random Multivariate Matrix Convolution Neural Network Model. Mathematical Problems in Engineering, 2022, 1-10, doi:10.1155/2022/5665636.
Zhao, B., & Liu, Y. (2022a). A Fuzzy Neural Network-Based Evaluation Method for Physical Education Teaching Management in Colleges. Computational Intelligence and Neuroscience, 2022, 1-10, doi:10.1155/2022/2365320.
Zhao, B., & Liu, Y. (2022b). Application of Data Warehouse Technology Based on Neural Network in Physical Education Quality Management. Mathematical Problems in Engineering, 2022, 1-10, doi:10.1155/2022/4456885.
Zhu, Y. (2022). Research on Education Teaching Quality Analysis Based on the Neural Network Model. Security and Communication Networks, 2022, 1-8, doi:10.1155/2022/2644032.