Author(s)

Xiaohan Zhu ¹, Huanpeng Liu ¹, Xin Peng ¹

Affiliation(s)

¹ School of Computer Science, National University of Defense Technology, Changsha, Hunan, 410000, China

Corresponding Author

Xiaohan Zhu

ABSTRACT

With the rapid development of online education, how to design a model for evaluating the similarity and difficulty of elementary math application problems has become an important research direction in the field of education. In order to measure the similarity and difficulty of elementary school math application problems, in this paper, after transforming the problems into feature vectors, the similarity of the two problems can be calculated by using measures such as cosine similarity and Euclidean distance. Then, six indicators, namely, the difficulty of the knowledge point of the problem, the difficulty of solving the problem, the logical difficulty of the problem, the linguistic difficulty of the problem, the difficulty of the picture of the problem, and the practicality of the problem, are selected, and the weights of each indicator are assigned by using the method of hierarchical analysis to determine the degree of importance of each indicator, which is used to determine the difficulty of the mathematical application problems at last.

KEYWORDS

Elementary School Math Problems, Cosine Similarity, Hierarchical Analysis

CITE THIS PAPER

Xiaohan Zhu, Huanpeng Liu, Xin Peng, A Study on Similarity and Difficulty Evaluation of Elementary School Mathematics Application Problems Based on Cosine Similarity and AHP. Curriculum and Teaching Methodology (2023) Vol. 6: 108-115. DOI: http://dx.doi.org/10.23977/curtm.2023.062017.

REFERENCES

[1] Mingzhe Li, Analysis of Elementary Math Word Problems Based on AI Deep Learning[J]. Academic Journal of Mathematical Sciences, 2023, 4(3)
[2] WU Man-yi, XU Liang-ji, ZHANG Kun. Research on the projected parameter seeking of probabilistic integration method based on SSA-BP neural network[J/OL]. Metal Mining: 1-10 [2022-05-03]. 
[3] ZHAO Lixing, HOU Xingming, XU Zhaowen, and LIN Zi. Equipment maintenance capability assessment based on GA-wavelet-BP neural network[J]. Modern Defense Technology, 2022, 50(02):84-95
[4] Jiang Qiyuan et al. Mathematical modeling [M]. Beijing: Higher Education Press, 2003. 
[5] WANG Xinmin, LI Weiying, ZHOU Yu, JING Xinyu, CHEN Sheng. Evaluation of surface water quality based on random forest and fuzzy comprehensive evaluation. [J]. Water Supply and Drainage, 2022, 58(02):128-132. 
[6] YANG Kun-Yu, PAN Jian, LUO Xian-Ting. Evaluation of multifunctional marching stool design scheme based on fuzzy comprehensive evaluation method[J]. Industrial Design, 2022(01):158-160. 
[7] Gao Feng, Wu Dan. Study on the application of cost control evaluation based on fuzzy comprehensive evaluation method--Taking the teaching building of A middle school in Tianjin as an example [J]. A Middle School Teaching Building in Tianjin [J]. Real Estate World, 2022(01):12-14. 
[8] WANG Guinan, SU Xianhe, SHANG Guojing, FAN Jiwei, BIAN Xian. State evaluation of generator excitation system based on fuzzy comprehensive evaluation Modeling and application[J]. Jilin Electric Power, 2021, 49(06):18-21. 
[9] JIN Yang, YU Minjian, YU Lu. Research on route generation assistance decision-making based on fuzzy comprehensive evaluation [J]. Science and Technology Innovation Science and Technology Innovation, 2021(34):73-75. 
[10] Zhong Hongxing. Fuzzy comprehensive evaluation of construction quality of DaDawang gate[J]. Heilongjiang Water Conservancy Science and Technology, 2021, 49(11):221- 225.

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