In this work, we propose a method for assessing the risk of low-achievement in secondary school with data collected from the Italian ministry of education. Low-achievement is a phenomenon whereby a student, despite completing his or her education, does not reach the level of competence expected by the school system. We train three machine learning models on a large, real dataset through the INVALSI large-scale assessment tests and compare the results in terms of predictive and descriptive performance. We exploit data collected in end-of-primary school mathematics tests to predict the risk of low-achievement at the end of compulsory schooling (5 years later). The promising results of our approach suggest that it is possible to generalise the methodology for other school systems and for different teaching subjects.

Andrea Zanellati, S.P.Z. (2023). Low-achievement risk assessment with machine learning. CEUR-WS.

Low-achievement risk assessment with machine learning

Andrea Zanellati
Primo
Writing – Original Draft Preparation
;
Stefano Pio Zingaro
Secondo
Writing – Original Draft Preparation
;
Maurizio Gabbrielli
Ultimo
Writing – Review & Editing
2023

Abstract

In this work, we propose a method for assessing the risk of low-achievement in secondary school with data collected from the Italian ministry of education. Low-achievement is a phenomenon whereby a student, despite completing his or her education, does not reach the level of competence expected by the school system. We train three machine learning models on a large, real dataset through the INVALSI large-scale assessment tests and compare the results in terms of predictive and descriptive performance. We exploit data collected in end-of-primary school mathematics tests to predict the risk of low-achievement at the end of compulsory schooling (5 years later). The promising results of our approach suggest that it is possible to generalise the methodology for other school systems and for different teaching subjects.
2023
Proceedings of the Ital-IA 2023 Thematic Workshops co-located with the 3rd CINI National Lab AIIS Conference on Artificial Intelligence
1
5
Andrea Zanellati, S.P.Z. (2023). Low-achievement risk assessment with machine learning. CEUR-WS.
Andrea Zanellati, Stefano Pio Zingaro, Maurizio Gabbrielli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/942496
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