In this paper, we propose a method for assessing the risk of low achievement in primary and secondary school. We train three machine learning models with data collected by the Italian Ministry of Education through the INVALSI large-scale assessment tests. We compare the results of the trained models and evaluate the effectiveness of the solutions in terms of performance and interpretability. We test our methods on 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

Student Low Achievement Prediction / Zanellati Andrea, Zingaro Stefano Pio, Gabbrielli Maurizio. - ELETTRONICO. - 13355:(2022), pp. 737-742. (Intervento presentato al convegno 23rd International Conference on Artificial Intelligence in Education, AIED 2022 tenutosi a Durham nel 27 - 31 July 2022) [10.1007/978-3-031-11644-5_76].

Student Low Achievement Prediction

Zanellati Andrea
;
Zingaro Stefano Pio;Gabbrielli Maurizio
2022

Abstract

In this paper, we propose a method for assessing the risk of low achievement in primary and secondary school. We train three machine learning models with data collected by the Italian Ministry of Education through the INVALSI large-scale assessment tests. We compare the results of the trained models and evaluate the effectiveness of the solutions in terms of performance and interpretability. We test our methods on 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
2022
Artificial Intelligence in Education. AIED 2022
737
742
Student Low Achievement Prediction / Zanellati Andrea, Zingaro Stefano Pio, Gabbrielli Maurizio. - ELETTRONICO. - 13355:(2022), pp. 737-742. (Intervento presentato al convegno 23rd International Conference on Artificial Intelligence in Education, AIED 2022 tenutosi a Durham nel 27 - 31 July 2022) [10.1007/978-3-031-11644-5_76].
Zanellati Andrea, Zingaro Stefano Pio, Gabbrielli Maurizio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/894629
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