Acta Scientific Computer Sciences

Review Article Volume 3 Issue 10

Evaluation of a Hybrid Principal Component Analysis and Cluster Analysis on Students' Learning Achievement and Motivation on Academic Courses

Chenyue Hong1, Zejun Hou1, Yahui Meng1* and Timothy Chen2

1Guangdong University of Petrochem Technol, Sch Sci, Peoples R China
2Division of Engineering and Applied Science, California Institute of Technology, Pasadena, USA

*Corresponding Author: Yahui Meng, Guangdong University of Petrochem Technol, Sch Sci, Peoples R China. Email: mengyahui@gdupt.edu.cn; Timothy Chen, Division of Engineering and Applied, Email: t13929751005@gmail.com

Received: June 02, 2021; Published: October 11, 2021

Abstract

Because the comprehensive evaluation system of College Students’ performance is not perfect, most schools take the examination results as the only standard to evaluate students, leading to the contemporary college students can only cope with the examination and lack of independent thinking ability and innovation. In this paper, principal component analysis and cluster analysis are used to evaluate students’ performance. Taking the real performance of 39 students in class 1 of information and computing science of Science College of Guangdong University of Petrochemical Technology in 2020-2021-1 semester as the evaluation object, this paper uses MATLAB software combined with two analysis methods to analyze more information about students’ performance, so as to evaluate students’ performance more scientifically, accurately, fairly and fairly..In this paper, principal component analysis and cluster analysis can help teachers improve their teaching quality and teach students in accordance with their aptitude. Through principal component analysis, from the perspective of professional courses and non professional courses, we can give students a relatively clear direction of employment or postgraduate entrance examination, help students find their strengths, and pursue innovative, unique and personalized development.

 

Keywords: Academic Courses; Principal Component Analysis; Cluster Analysis; MATLAB

 

Bibliography

  1. Fan Shuangnan., et al. “Comprehensive evaluation of students' usual performance in computer foundation course based on principal component analysis”. Light Industry and Technology10 (2018): 175-176.
  2. Li Zhenliang and Zhang Zhenqi. “Comprehensive evaluation of students' performance in Independent Colleges Based on principal component analysis”. Science and Technology Horizon 18(2018): 107-109.
  3. Shang Lihua. “Application of fuzzy clustering analysis algorithm in comprehensive evaluation of student performance”. Computer Knowledge and Technology5 (2014): 1024-1026.
  4. Qi Zonghui. “Application of principal component cluster analysis in comprehensive evaluation of students' performance”. Journal of Taiyuan City Polytechnic 8 (2016): 182-183.
  5. Liu Lu., et al. “Application of principal component cluster analysis in comprehensive evaluation of student achievement”. Journal of Liaoning University of Technology (NATURAL SCIENCE EDITION) 32.3 (2012): 200-204.

Citation

Citation: Yahui Meng., et al. “Evaluation of a Hybrid Principal Component Analysis and Cluster Analysis on Students' Learning Achievement and Motivation on Academic Courses". Acta Scientific Computer Sciences 3.10 (2021): 02-05.

Copyright

Copyright: © 2021 Yahui Meng., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate35%
Acceptance to publication20-30 days

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