The early diagnosis of gaps in students preparing to leave secondary school is important for their entry into the labour market or for their academic experience. The present paper iinvestigates a specific aspect of the skills of schoolleavers, namely mathematical literacy. Quantile regression is proposed to explore the impact of students’ characteristics and social context on mathematical literacy, taking into account the ubiquitous heterogeneity of the students’ population. The proposed study grounds on Invalsi test results.
Modeling the main drivers of mathematical literacy of school-leaving students. Some evidence from the Invalsi tests / Davino, Cristina; Palumbo, Francesco; Romano, Rosaria; Vistocco, Domenico. - (2023), pp. 484-489. (Intervento presentato al convegno 11th International Conference IES 2023 Statistical Methods for Evaluation and Quality: Techniques, Technologies and Trends (T3) tenutosi a Pescara nel 30 agosto 2023- 1 settembre 2023) [10.60984/978-88-94593-36-5-IES2023].
Modeling the main drivers of mathematical literacy of school-leaving students. Some evidence from the Invalsi tests
Cristina Davino;Francesco Palumbo;Rosaria Romano;Domenico Vistocco
2023
Abstract
The early diagnosis of gaps in students preparing to leave secondary school is important for their entry into the labour market or for their academic experience. The present paper iinvestigates a specific aspect of the skills of schoolleavers, namely mathematical literacy. Quantile regression is proposed to explore the impact of students’ characteristics and social context on mathematical literacy, taking into account the ubiquitous heterogeneity of the students’ population. The proposed study grounds on Invalsi test results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
