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Autor/inn/enThomas, Emily H.; Galambos, Nora
TitelWhat Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis
QuelleIn: Research in Higher Education, 45 (2004) 3, S.251-269 (19 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0361-0365
DOI10.1023/B:RIHE.0000019589.79439.6e
SchlagwörterStudent Attitudes; Multiple Regression Analysis; Student Experience; Satisfaction; Student Characteristics; Social Integration; Academic Achievement; Predictor Variables; College Faculty; Data Analysis; Student Interests; Interest Inventories
AbstractTo investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The three measures have different predictors and cannot be used interchangeably. Academic experiences are influential. In particular, faculty preparedness, which has a well-known relationship to student achievement, emerges as a principal determinant of satisfaction. Social integration and pre-enrollment opinions are also important. Campus services and facilities have limited effects, and students' demographic characteristics are not significant predictors. Decision tree analysis reveals that social integration has more effect on the satisfaction of students who are less academically engaged. (Author).
AnmerkungenSpringer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com.
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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