Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/20032
Autoria: Guerra, M.
Bassi, F.
Dias, J. G.
Data: 2020
Título próprio: A multiple-indicator latent growth mixture model to track courses with low-quality teaching
Volume: 147
Número: 2
Paginação: 361 - 381
ISSN: 0303-8300
DOI (Digital Object Identifier): 10.1007/s11205-019-02169-x
Palavras-chave: Higher education
Quality of didactics
Latent growth mixture models
Outlier detection
Synthetic indicator
Data science
Resumo: 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.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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