Modeling university student satisfaction: the case of the humanities and social studies degree programs

Modeling university student satisfaction: the case of the humanities and social studies degree... This paper investigates the problem of modeling students’ satisfaction ratings of various aspects of academic teaching in six humanities departments at University of Naples Federico II. In particular, we propose a strategy for analyzing data from the annual survey used to collects feedback from students across the university. The statistical procedure for this data analysis consists of two steps. First, the random forest method is fitted to the data to identify important predictors of student global satisfaction. Second, the probability distribution of student satisfaction ratings is estimated by fitting a mixture distribution with varying parameters (denoted the CUB model). The random forest methods shows that students’ interest in the course topics, together with the course objectives and teaching tools, are the main determinants of student satisfaction. Inclusion of these covariates in the CUB models confirms their dominant role in differentiating students’ evaluations of degree courses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Modeling university student satisfaction: the case of the humanities and social studies degree programs

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0428-9
Publisher site
See Article on Publisher Site

Abstract

This paper investigates the problem of modeling students’ satisfaction ratings of various aspects of academic teaching in six humanities departments at University of Naples Federico II. In particular, we propose a strategy for analyzing data from the annual survey used to collects feedback from students across the university. The statistical procedure for this data analysis consists of two steps. First, the random forest method is fitted to the data to identify important predictors of student global satisfaction. Second, the probability distribution of student satisfaction ratings is estimated by fitting a mixture distribution with varying parameters (denoted the CUB model). The random forest methods shows that students’ interest in the course topics, together with the course objectives and teaching tools, are the main determinants of student satisfaction. Inclusion of these covariates in the CUB models confirms their dominant role in differentiating students’ evaluations of degree courses.

Journal

Quality & QuantitySpringer Journals

Published: Oct 4, 2016

References

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