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Social Indicators Research

, Volume 147, Issue 2, pp 361–381 | Cite as

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

  • Marco Guerra
  • Francesca BassiEmail author
  • José G. Dias
Original Research
  • 87 Downloads

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 

Notes

Acknowledgements

The authors would like to thank the editor and three anonymous reviewers for their constructive comments, which helped us to improve the manuscript. This work was funded by the Portuguese Foundation for Science and Technology (Grant UID/GES/00315/2013 and UID/GES/00315/2019) and by Grant BIRD162088/16 financed by the University of Padua for the project entitled “Advances in Multilevel and Longitudinal Modelling”.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Statistical SciencesUniversity of PaduaPaduaItaly
  2. 2.Business Research Unit (BRU-IUL)Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal

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