Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

Autor/inn/en	Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan
Titel	Principal Component Clustering Approach to Teaching Quality Discriminant Analysis
Quelle	In: Cogent Education, 3 (2016) 1, Artikel 1194553 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2331-186X
DOI	10.1080/2331186X.2016.1194553
Schlagwörter	Discriminant Analysis; Factor Analysis; Student Evaluation of Teacher Performance; Instructional Effectiveness; Cluster Grouping; College Programs; Measurement Techniques; Mathematical Models; Evaluation Methods; Educational Quality; Foreign Countries; China + Suchen Sie Ihr Suchwort? Diskriminanzanalyse; Faktorenanalyse; Unterrichtserfolg; Eingruppierung; Studienprogramm; Messtechnik; Mathematical model; Mathematisches Modell; Quality of education; Bildungsqualität; Ausland
Abstract	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. (As Provided).
Anmerkungen	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: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01