Practice of Data Mining in Formative Evaluation
American Journal of Applied Mathematics
Volume 6, Issue 2, April 2018, Pages: 78-86
Received: Jun. 25, 2018;
Published: Jun. 26, 2018
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Lintong Zhang, College of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
Na Li, College of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
Zhigang Zhang, College of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
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The main purpose of this paper is to use the students' English learning situation on the Internet to formally evaluate the students' final English performance level. First of all, we introduce the concept of formative evaluation, and the principles of three kinds of data mining algorithms: naive Bayes classification, C4.5 decision tree, and Logistic regression; then, we use the student online learning data table to achieve the key calculation process of the above algorithm; Further, we use Matlab programming to predict the student's final grade level and compare the performance of each algorithm. Practice shows that, C4.5 performs better than Naive Bayes algorithm on predicting the four classifications of grades (great/good/medium/bad), but the accuracy is not very high; Naive Bayes performs better than the other two algorithms and has higher accuracy on predicting the two classifications of grades (good/bad). Considering the two factors of duration of online learning and number of submissions, the accuracy of the prediction has not been significantly improved. Therefore, there is no need to consider both in terms of this formative assessment. Formative assessment has a very important significance in teaching, and plays a key role in motivating students' learning and teacher guidance. According to the forecast results, it can provide some help and guidance for students' follow-up study, so as to improve students' learning effect.
Data Mining, Formative Evaluation, Algorithm Performance
To cite this article
Practice of Data Mining in Formative Evaluation, American Journal of Applied Mathematics.
Vol. 6, No. 2,
2018, pp. 78-86.
Ding Bo. Research on subdivision prediction of online learning students' academic achievement [D]. Jiangnan University, 2016.
Cheng Kefei, Zhang Cong. Naive Bayesian Classifier Based on Feature Weighting [J]. Computer Simulation, 2006, 23 (10):92-94.
Mladenic D, Grobelnik M. Feature Selection for Unbalanced Class Distribution and Naive Bayes [C]. Sixteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc. 1999:258-267.
Rish I. An empirical study of the naive Bayes classifier [J]. Journal of Universal Computer Science, 2001, 1 (2):127.
Quinlan J. R. C4.5: programs for machine learning [M]. Morgan Kaufmann Publishers Inc. 1993.
Quinlan J R. Improved Use of Continuous Attributes in C4.5 [J]. Journal of Artificial Intelligence Research, 1996, 4 (1):77-90.
Wang Xiaoguo, Huang Yukun, Zhu Wei, et al. Application of C4.5 Algorithm to Construct Customer Classification Decision Tree [J]. Computer Engineering, 2003, 29 (14):89-91.
Choi In-ho. The application of data mining in student professional achievement prediction [J]. Software, 2016, (01): 24-27.
Wang Jichuan, Guo Zhigang. Logistic Regression Model: Method and Application [M]. Higher Education Press, 2001.
Zheng Yinan, Cao Peihua, Ou Chunquan. Realization of N:M Conditional Logistic Regression Analysis on Statistical Software [J]. Chinese Journal of Health Statistics, 2011, 28 (1):93-94.
Tan Hongwei, Zeng Jie. Impact Analysis of Logistic Regression Model [J]. Mathematical Statistics and Management, 2013, 32 (3): 476-485.
Zhang Xiaodan. Application of Educational Data Mining Technology in the Course of "University Computer Foundation" [D]. Inner Mongolia Normal University, 2017.