Scientific journal paper Q1
A multiple-indicator latent growth mixture model to track courses with low-quality teaching
Marco Guerra (Guerra, M.); Francesca Bassi (Bassi, F.); José G. Dias (Dias, J. G.);
Journal Title
Social Indicators Research
Year (definitive publication)
2020
Language
English
Country
Netherlands
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Web of Science®

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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.
Acknowledgements
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Keywords
Higher education,Quality of didactics,Latent growth mixture models,Outlier detection,Synthetic indicator,Data science
  • Other Social Sciences - Social Sciences
Funding Records
Funding Reference Funding Entity
UID/GES/00315/2013 Fundação para a Ciência e a Tecnologia
D162088/16 Fundação para a Ciência e a Tecnologia
UID/GES/00315/2019 Fundação para a Ciência e a Tecnologia

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