https://orcid.org/0000-0002-7735-5040
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ABSTRACT
Students’ attainment in mathematics is associated with several personal and contextual factors, but little research has been carried out to explore the intersectionality between them. The current paper aims to fill this gap. Census assessment data collected in Italy at Grade 10 (on average, 15-year-old students) by the Italian national institute for the evaluation of the educational system were analysed via a multilevel regression model to account for data hierarchy (422,865 students in 24,279 classrooms, in 3,950 schools). Census data in educational research allowed exploration of the intersectionality between independent variables at different levels of the model’s hierarchy. Results showed that reading skills mediate the relationship between students’ attainment in mathematics and their gender, citizenship status, and socioeconomic status (SES). Implications for policy and practice are discussed.
Acknowledgement
I would like to thank the European Commission for supporting this work and INVALSI for the data. Nevertheless, the views expressed in this work are attributable exclusively to the author and, thereby, in citing the work, it is not appropriate to attribute the arguments expressed herein to the European Commission or to INVALSI.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1 Ability is a technical term used within the framework of the Rasch analysis (Rasch, Citation1960) to refer to the latent trait measured by the tests used to collect data.
2 Although all school types allow access to university, Tecnici and Professionali offer a specific education/training and a direct access to the job market in a variety of sectors that do not require an academic degree (e.g., IT consultancy, hotel and catering services), whereas Licei offer a broader education that acts as a preparation for university.
3 After having analysed the pattern of students’ answers, plausible values of mean and variance of classroom scores are calculated by comparing data collected in sample classrooms with data collected in non-sample classrooms. More precisely, Quintano et al. (Citation2009) developed a two-stage method, focused on the presence of outlier units at classroom level. At the first stage, students with too many missing or invalid answers were excluded; and then classrooms of students with a very high average score and/or with the within variability close to zero were detected through a factorial analysis. At the second stage, a fuzzy procedure was used to detect outlier units at the classroom level, and in such a way calibrating the correction. The correction coefficient is used by INVALSI to weight the average score of each classroom.
4 Students’ ability in reading is defined as an individual’s capacity to understand, use, reflect on, and engage with written texts, to pursue personal goals, to develop one’s knowledge and potential, and to participate in society (INVALSI, Citation2018).
5 In Italy, usually students enrol in primary school at age 6 (these are called “regular students”), but pupils who are born between January and March and are still 5 years old can access primary school early (in this case, they are called “in advance” students). Students repeating a grade are called “retained”.
6 According to the definition provided by INVALSI, “first-generation students” are foreign-born students (i.e., not born in Italy) whose parents were also foreign born; whereas “second-generation students” are students born in Italy whose parents were born not in Italy (INVALSI, Citation2017).
7 Students taking just one of the two achievement tests were excluded.
8 The interaction term between SES and school type is statistically significant in Licei but not statistically significant in Professionali, where SES is very low and its variability is lower than in the other two school types ().
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Clelia Cascella
Clelia Cascella holds a PhD in Research Methodology and a PhD in Economics. She was formerly a Marie Curie Research Fellow at Manchester Institute of Education and was then appointed as a Lecturer in Social Statistics at the University of Manchester. Clelia Cascella’s main research interest is in (mathematics) education focusing on gender differences in learning mathematics, learners’ attitudes and dispositions and their relationship with teaching practices. Most of Clelia Cascella’s research activity is based on secondary data from national and international surveys and spread across various areas of social research methodology and advanced quantitative methods, including measurement and assessment with focus on the use of the Rasch model and other item response theory models and longitudinal and multilevel modelling.