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ERIC Number: EJ1138389
Record Type: Journal
Publication Date: 2016
Pages: 11
Abstractor: As Provided
ISSN: EISSN-2331-186X
Principal Component Clustering Approach to Teaching Quality Discriminant Analysis
Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan
Cogent Education, v3 n1 Article 1194553 2016
Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET by clustering the result of extracting the indexes through the principal component analysis (PCA), then we also test the rationality of the rating using Fisher's discriminant function. Finally, the model and algorithm are proved to be effective and objective according to the empirical analysis.
Cogent OA. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: China
Grant or Contract Numbers: N/A