Assessing maths learning gaps using Italian longitudinal data

Silvia Bianconcini (), Stefania Mignani and Jacopo Mingozzi
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Stefania Mignani: Department of Statistical Sciences - University of Bologna
Jacopo Mingozzi: Department of Statistical Sciences - University of Bologna

Statistical Methods & Applications, 2023, vol. 32, issue 3, No 9, 930 pages

Abstract: Abstract In the educational context, one of the main goals is to reduce the disparities among students, generally at the national level, to allow all individuals to achieve a similar cultural background. Using data from a large-scale standardised test administered by INVALSI (National Institute for the Evaluation of the Educational System), this paper offers a first longitudinal analysis of the performance in the maths test of a cohort of students enrolled in 2013/2014 at grade 8 and observed up to grade 13. The aim is to identify those obstacles that undermine students’ learning to help adopt informed educational actions. Specific features of these data are their hierarchical structure and the presence of not vertically scaled scores. Two approaches have been followed for their analysis: growth models and growth percentiles. Coherently with the literature, our results suggest the presence of a gender gap, a significant impact of the type of school, and of social-cultural background. Differently from previous research on the INVALSI data, we evaluate these time-invariant covariates’ effects on students’ performance over different school cycles.

Keywords: INVALSI maths test; Longitudinal data; Student growth percentiles; Gender gap; Cross-cultural differences; School achievement (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-022-00676-9

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