Abstract
Accurate analysis and prediction of student achievement is of great significance for improving the quality of teaching. By taking the student's scores as the experimental data, this paper proposes a BP neural network based student learning performance prediction method, which adopts four gradients of SGD, Momentum, AdaGrad and Adam. By comparing with the traditional machine learning algorithm of random forest, the effectiveness of proposed method is verified, and the prediction of students' learning performance and the early warning of abnormal situation of the students’ learning performances are finally implemented, which helps teachers to make more efforts to improve worse situation of learning performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rifat, M.R.I., Al Imran, A., Badrudduza, A.S.M.: Educational performance analytics of undergraduate business students. Int. J. Modern Educ. Comput. Sci. (IJMECS) 11(7), 44–53 (2019). https://doi.org/10.5815/ijmecs.2019.07.05
Yan, S.: Analysis of the causes of alienation of modern information technology and the possibility of its elimination. Modern Educ. Technol. 1, 8–11 (2009)
Vijayalakshmi, V., Venkatachalapathy, K.: Comparison of predicting student’s performance using machine learning algorithms. Int. J. Intell. Syst. Appl. (IJISA) 11(12), 34–45 (2019). https://doi.org/10.5815/ijisa.2019.12.04
Song, Y.: Study on evaluation of students’ achievement based on random forest. J. Qiqihar Univ. 11, 1–4 (2017)
Djulovic, A., Li, D.: Towards freshman retention prediction: a comparative study . Int. J. Inf. Educ. Technol. 5, 494–500 (2013)
Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Predicting students’ performance in distance learning using machine learning techniques . Appl. Artif. Intell. 5, 411–426 (2004)
Mahedy, A.S., Abdelsalam, A.A., Mohamed, R.H., El-Nahry, I.F.: Utilizing neural networks for stocks prices prediction in stocks markets. Int. J. Inf. Technol. Comput. Sci. (IJITCS), 12(3), 1–7 (2020). https://doi.org/10.5815/ijitcs.2020.03.01
Romero, C., Espejo, P.G., Zafra, A., et al.: Web usage mining for predicting final marks of students that use moodle courses . Comput. Appl. Eng. Educ. 1, 135–146 (2013)
Ben-Hur, A., Siegelmann, H.T., et al.: Support vector clustering . J. Mach. Learn. Res. 2(2), 125–137 (2002)
Noble, W.S.: What is a support vector machine. Nature Biotechnol. 24(12), 1565–1567 (2006)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (61772180); the Hubei Provincial Technology Innovation Project (2019AAA047).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pan, D., Wang, S., Jin, C., Yu, H., Hu, C., Wang, C. (2021). Research on Student Achievement Prediction Based on BP Neural Network Method. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education IV. AIMEE 2020. Advances in Intelligent Systems and Computing, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-67133-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-67133-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67132-7
Online ISBN: 978-3-030-67133-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)