The Application of Three Machine Learning Algorithms in Student Performance Evaluation
Article No.: 93, Pages 1 - 5
Abstract
At present, the research of machine learning is a hot topic. In this paper, three machine learning algorithms, decision tree, support vector machine and random forest, are used to predict the students' achievement data sets. The results in the early stage of the data were analyzed to predict the average results in the later stage of the professional courses. The results show that the classification performance of the three classifier models is high, among which the random forest classifier is the best in the accuracy rate, precision rate, recall rate and F1 value. Moreover, the comprehensive forecast result and the course importance order can guide the student to carry on the pertinence remediation, and it's helpful for students to make specific explanations in class.
References
[1]
Ma Li (2016). Research on Optimization and Improvement of Random Forests Algorithm. Doctoral thesis: Jinan University.
[2]
Wu Qiang, Fang Rui, Han Bin, et al. (2018). Research and Analysis on Students' Achievement and Prediction Model based on Decision Tree and LMBP Neural Network. Journal of Chengdu University of Information Technology, 2018(3), 274--280.
[3]
Sun Li and Zhang Kai (2016). Research and realization of subdivision prediction of learning performance in network education based on data mining. Distance Education in China, 2016(12), 22--29.
[4]
M Turker and D Koc-San (2015). Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. International Journal of Applied Earth Observation and Geoinformation, 2015(34), 58--69.
[5]
Jair Cervantes, Farid Garcia Lamont, Asdrubal Lopez-Chau, et al. (2015). Dataselection based on decision tree for SVM classification on large data sets. Applied Soft Computing, 2015(37), 787--798.
[6]
Pengfei Li, Limin Dong, Huachao Xiao, et al. (2015). A cloud image detection method based on SVM vector machine. Neurocomputing, 2015(169), 34--42.
[7]
Verikas A, Gelzinis A and Bacauskiene M (2011). Mining data with random forests: a survey and resultsof new tests. Pattern Recognition, 44(2), 330--349.
[8]
Assuncao J, Fernandes P, Lopes L, et al. (2013). Distributed stochastic aware random forests-efficientdata mining for big data. IEEE International Congress on Big Data, Santa: Clara, 425--426.
[9]
Schulter S, Leistner C, Wohlhart P, et al. (2014). Accurate object detection with joint classification-regression random forests. 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 923--930.
[10]
Mahapatra D (2014). Analyzing training information from random forests for improved image seg-mentation[J]. IEEE Transactions on Image Processing, 23(4), 1504--1512.
[11]
Zhang L and Suganthan P N (2014). Random forests with ensemble of feature spaces. Pattern Recognition, 47(10), 3429--3437.
[12]
Cao Zhengfeng (2014). Study 0n optimization of random forest algorithm. Doctoral thesis: Capital University of Economics and Business.
[13]
Liang Qiongfang and Sha Ren (2019). Classification of Mathematics Testability Difficulty Based on Random Forest. Software Guide, 9, 5--9.
[14]
Feng Xioarong (2019). Feature selection for high dimensional data based on deep learning and random forest. Computer Engineering and Design, 40(9), 2494--2501
[15]
Yang Changchun (2019). Feature selection method for constructing student profile based on random forest[J]. Computer Engineering and Design, 40(10), 2827--2834
[16]
Lv Hongyan and Feng Qian (2019). A review of random forests algorithm. Journal of the Hebei Academy of Sciences, 36(3), 37--41.
Index Terms
- The Application of Three Machine Learning Algorithms in Student Performance Evaluation
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Published In
October 2020
1038 pages
ISBN:9781450377720
DOI:10.1145/3424978
- Conference Chair:
- Ali Emrouznejad,
- Program Chair:
- Jui-Sheng Rayson Chou
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 October 2020
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CSAE 2020
CSAE 2020: The 4th International Conference on Computer Science and Application Engineering
October 20 - 22, 2020
Sanya, China
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CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;
Overall Acceptance Rate 368 of 770 submissions, 48%
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