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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Guerra, M., Bassi, F. & Dias, J. G. (2020). A multiple-indicator latent growth mixture model to track courses with low-quality teaching. Social Indicators Research. 147 (2), 361-381
Exportar Referência (IEEE)
M. Guerra et al.,  "A multiple-indicator latent growth mixture model to track courses with low-quality teaching", in Social Indicators Research, vol. 147, no. 2, pp. 361-381, 2020
Exportar BibTeX
@article{guerra2020_1660414662428,
	author = "Guerra, M. and Bassi, F. and Dias, J. G.",
	title = "A multiple-indicator latent growth mixture model to track courses with low-quality teaching",
	journal = "Social Indicators Research",
	year = "2020",
	volume = "147",
	number = "2",
	doi = "10.1007/s11205-019-02169-x",
	pages = "361-381",
	url = "https://link.springer.com/article/10.1007%2Fs11205-019-02169-x#rightslink"
}
Exportar RIS
TY  - JOUR
TI  - A multiple-indicator latent growth mixture model to track courses with low-quality teaching
T2  - Social Indicators Research
VL  - 147
IS  - 2
AU  - Guerra, M.
AU  - Bassi, F.
AU  - Dias, J. G.
PY  - 2020
SP  - 361-381
SN  - 0303-8300
DO  - 10.1007/s11205-019-02169-x
UR  - https://link.springer.com/article/10.1007%2Fs11205-019-02169-x#rightslink
AB  - 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.
ER  -