Practice of Data Mining in Formative Evaluation

Lintong Zhang; Na Li; Zhigang Zhang

doi:doi:10.11648/j.ajam.20180602.18

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Practice of Data Mining in Formative Evaluation

Lintong Zhang, Na Li, Zhigang Zhang

Published in American Journal of Applied Mathematics (Volume 6, Issue 2)

Received: 25 June 2018 Published: 26 June 2018

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Abstract

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.

Published in	American Journal of Applied Mathematics (Volume 6, Issue 2)
DOI	10.11648/j.ajam.20180602.18
Page(s)	78-86
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Data Mining, Formative Evaluation, Algorithm Performance

References

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[2]	Cheng Kefei, Zhang Cong. Naive Bayesian Classifier Based on Feature Weighting [J]. Computer Simulation, 2006, 23 (10):92-94.
[3]	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.
[4]	Rish I. An empirical study of the naive Bayes classifier [J]. Journal of Universal Computer Science, 2001, 1 (2):127.
[5]	Quinlan J. R. C4.5: programs for machine learning [M]. Morgan Kaufmann Publishers Inc. 1993.
[6]	Quinlan J R. Improved Use of Continuous Attributes in C4.5 [J]. Journal of Artificial Intelligence Research, 1996, 4 (1):77-90.
[7]	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.
[8]	Choi In-ho. The application of data mining in student professional achievement prediction [J]. Software, 2016, (01): 24-27.
[9]	Wang Jichuan, Guo Zhigang. Logistic Regression Model: Method and Application [M]. Higher Education Press, 2001.
[10]	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.
[11]	Tan Hongwei, Zeng Jie. Impact Analysis of Logistic Regression Model [J]. Mathematical Statistics and Management, 2013, 32 (3): 476-485.
[12]	Zhang Xiaodan. Application of Educational Data Mining Technology in the Course of "University Computer Foundation" [D]. Inner Mongolia Normal University, 2017.

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APA Style

Lintong Zhang, Na Li, Zhigang Zhang. (2018). Practice of Data Mining in Formative Evaluation. American Journal of Applied Mathematics, 6(2), 78-86. https://doi.org/10.11648/j.ajam.20180602.18

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Lintong Zhang; Na Li; Zhigang Zhang. Practice of Data Mining in Formative Evaluation. Am. J. Appl. Math. 2018, 6(2), 78-86. doi: 10.11648/j.ajam.20180602.18

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AMA Style

Lintong Zhang, Na Li, Zhigang Zhang. Practice of Data Mining in Formative Evaluation. Am J Appl Math. 2018;6(2):78-86. doi: 10.11648/j.ajam.20180602.18

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@article{10.11648/j.ajam.20180602.18,
  author = {Lintong Zhang and Na Li and Zhigang Zhang},
  title = {Practice of Data Mining in Formative Evaluation},
  journal = {American Journal of Applied Mathematics},
  volume = {6},
  number = {2},
  pages = {78-86},
  doi = {10.11648/j.ajam.20180602.18},
  url = {https://doi.org/10.11648/j.ajam.20180602.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20180602.18},
  abstract = {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.},
 year = {2018}
}

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TY  - JOUR
T1  - Practice of Data Mining in Formative Evaluation
AU  - Lintong Zhang
AU  - Na Li
AU  - Zhigang Zhang
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ajam.20180602.18
DO  - 10.11648/j.ajam.20180602.18
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 78
EP  - 86
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20180602.18
AB  - 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.
VL  - 6
IS  - 2
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Lintong Zhang, Na Li, Zhigang Zhang. (2018). Practice of Data Mining in Formative Evaluation. American Journal of Applied Mathematics, 6(2), 78-86. https://doi.org/10.11648/j.ajam.20180602.18

Copy | Download

ACS Style

Lintong Zhang; Na Li; Zhigang Zhang. Practice of Data Mining in Formative Evaluation. Am. J. Appl. Math. 2018, 6(2), 78-86. doi: 10.11648/j.ajam.20180602.18

Copy | Download

AMA Style

Lintong Zhang, Na Li, Zhigang Zhang. Practice of Data Mining in Formative Evaluation. Am J Appl Math. 2018;6(2):78-86. doi: 10.11648/j.ajam.20180602.18

Copy | Download

@article{10.11648/j.ajam.20180602.18,
  author = {Lintong Zhang and Na Li and Zhigang Zhang},
  title = {Practice of Data Mining in Formative Evaluation},
  journal = {American Journal of Applied Mathematics},
  volume = {6},
  number = {2},
  pages = {78-86},
  doi = {10.11648/j.ajam.20180602.18},
  url = {https://doi.org/10.11648/j.ajam.20180602.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20180602.18},
  abstract = {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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Practice of Data Mining in Formative Evaluation
AU  - Lintong Zhang
AU  - Na Li
AU  - Zhigang Zhang
Y1  - 2018/06/26
PY  - 2018
N1  - https://doi.org/10.11648/j.ajam.20180602.18
DO  - 10.11648/j.ajam.20180602.18
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 78
EP  - 86
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20180602.18
AB  - 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.
VL  - 6
IS  - 2
ER  -

Copy | Download