Welcome to Francis Academic Press

Frontiers in Educational Research, 2023, 6(9); doi: 10.25236/FER.2023.060923.

Research on Neural Network Prediction System in Loose-leaf Textbook Course Assessment

Author(s)

Liu Xing1,2, Wu Sheng1,2, Chen Li1,2, Mei Jing1,2, Hu Dong1

Corresponding Author:
Liu Xing
Affiliation(s)

1College of Mechanical Engineering, Sichuan Vocational College of Chemical Industry, Luzhou, 646300, China

2The Key Laboratory of Mechanical Structure Optimization & Material Application Technology of Luzhou, Sichuan Vocational College of Chemical Industry, Luzhou, 646300, China

Abstract

The unique advantages of flexible and novel loose-leaf textbooks have become a new development direction, but the diversified assessment system of loose-leaf textbooks is still relatively imperfect, and the application of traditional curriculum assessment systems in loose-leaf textbooks is subject to certain constraints. Therefore, in order to improve the diversified assessment system of loose-leaf textbooks, this paper proposes a machine learning algorithm applied to the loose-leaf textbook assessment system. By comparing various neural network algorithms, a prediction model suitable for the diversified assessment system was found. The results show that the application of SVM radial basis function neural network in the assessment and prediction system for loose-leaf courses has high prediction accuracy and small error, can effectively play a prediction and reminder role, improve the diversified assessment system, and promote the application and promotion of loose-leaf textbooks.

Keywords

neural network algorithm; loose-leaf teaching materials; course assessment system

Cite This Paper

Liu Xing, Wu Sheng, Chen Li, Mei Jing, Hu Dong. Research on Neural Network Prediction System in Loose-leaf Textbook Course Assessment. Frontiers in Educational Research (2023) Vol. 6, Issue 9: 139-145. https://doi.org/10.25236/FER.2023.060923.

References

[1] Li Xiaoyin. Exploration of Curriculum Construction in Vocational Colleges under the Background of "Three Education" Reform—Taking the Course of Comprehensive Practical Training in Finance and Accounting as an Example [J]. Accounting Learning, 2021 (29): 163-164

[2] Zhu Haihui. Practice and Thinking on the Development of Loose-leaf Textbook Based on Automobile Maintenance Course [J]. Automotive Maintenance and Repair, 2021 (20): 17-19

[3] Cui Chenghong, Xia Jianwei, Wang Jing. Practical exploration on the development of three-dimensional new loose-leaf teaching materials for pharmaceutical technology specialty courses in higher vocational colleges [J]. Journal of Qingdao Vocational and Technical College, 2020, 33 (3): 5.

[4] Wu Lei, Fang Bin, Diao Liping, et al. A Resampling Method for Unbalanced Data Combining Oversampling and Undersampling [J]. Computer Engineering and Applications, 2013, 49 (21): 6.

[5] Ding Yi, Yang Jian. Comparison of Euclidean distance and normalized Euclidean distance in k-nearest neighbor algorithm [J]. Software, 2020, 41 (10): 3.

[6] Zhang Qian, Yang Yaoquan. Soft Measurement of Oxygen Content in Flue Gas of Thermal Power Plants Based on Support Vector Machine Regression [J]. Information and Control, 2013: 10.

[7] Zhou Kaili. Neural Network Model and MATLAB Simulation Program Design [M]. Tsinghua University Press, 2005.

[8] Zheng Fengxia. Research on Prediction Methods and Applications Based on Neural Networks and Time Series [D]. University of Electronic Science and Technology, 2013.

[9] Kouser R R, Manikandan T, Kumar V V. Heart Disease Prediction System Using Artificial Neural Network, Radial Basis Function and Case Based Reasoning[J]. Journal of Computational and Theoretical Nanoscience, 2018, 15(9):2810-2817.

[10] Lu Bo. Research on engine/aircraft integrated modeling and parameter estimation technology based on Matlab [D]. Nanjing University of Aeronautics and Astronautics 2013.