A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching

Marco Guerra, Francesca Bassi () and José G. Dias
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Marco Guerra: University of Padua
Francesca Bassi: University of Padua
José G. Dias: Instituto Universitário de Lisboa (ISCTE-IUL)

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2020, vol. 147, issue 2, No 1, 381 pages

Abstract: Abstract This paper describes a multi-indicator latent growth mixture model built on the data collected by a large Italian university to track students’ satisfaction over time. The analysis of the data involves two steps: first, a pre-processing of data selects the items to be part of the synthetic indicator that measures students’ satisfaction; the second step then retrieves heterogeneity that allows the identification of a clustering structure with a group of university courses (outliers) which underperform in terms of students’ satisfaction over time. Regression components of the model identify courses in need of further improvement and that are prone to receiving low classifications from students. Results show that it is possible to identify a large group of didactic activities with a high satisfaction level that stays constant over time; there is also a small group of problematic didactic activities with low satisfaction that decreases over the period under analysis.

Keywords: Higher education; Quality of didactics; Latent growth mixture models; Outlier detection; Synthetic indicator; Data science (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11205-019-02169-x

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