Earticle

초록

영어

Teachers and students' performances are of great importance in education. However, how to evaluate teachers' works and students' academic levels are extremely difficult and complex because it contains various effects of weights that should be used to assess the achievements. Also, education workers often find it difficult to manipulate the large-scale data while evaluating the education works and students' performances. To address this problem, we used machine learning techniques to develop two groups of models for evaluating teachers and students' performances respectively. Using artificial neural networks (ANNs) can ensure the accuracy and fairness of the evaluation works. Our results successfully proved that general regression neural network (GRNN) model can effectively generate the robust responses to analyze different independent variables and give out correct results to distinguish different achievements done by teachers and students.

목차

Abstract
 1. Introduction
 2. Artificial Neural Network
 3. Model Development
 4. Results and Discussions
  4.1. Evaluation Models for Teachers
  4.2. Evaluation Models for Teachers
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Xiao Qianyin [ Foreign Language School, Southwest Petroleum University, Chengdu Sichuan, 610500, China ]
• Liu Bo [ Department of Planning and Evaluation (Teacher Education and Development Center), Southwest Petroleum University, Chengdu Sichuan, 610500, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605