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.
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Notes
- 1.
OECD stands for Organization for Economic Co-operation and Development.
References
Al-Barrak, M.A., Al-Razgan, M.: Predicting students final GPA using decision trees: a case study. Int. J. Inf. Educ. Technol. 6(7), 528 (2016)
Albreiki, B., Zaki, N., Alashwal, H.: A systematic literature review of student performance prediction using machine learning techniques. Educ. Sci. 11(9), 552 (2021)
Alexander, K.L., Entwisle, D.R., Olson, L.S.: Schools, achievement, and inequality: a seasonal perspective. Educ. Eval. Policy Anal. 23(2), 171–191 (2001)
Baartman, L.K., De Bruijn, E.: Integrating knowledge, skills and attitudes: conceptualising learning processes towards vocational competence. Educ. Res. Rev. 6(2), 125–134 (2011)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cassen, R., Kingdon, G.: Tackling Low Educational Achievement. Joseph Rowntree Foundation (2007)
Curtis, D.D., McMillan, J.: School non-completers: profiles and initial destinations (2008)
Geary, D.C.: Consequences, characteristics, and causes of mathematical learning disabilities and persistent low achievement in mathematics. J. Dev. Behav. Pediatr. JDBP 32(3), 250 (2011)
Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data (2021)
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., Navarro-Colorado, B.: A systematic review of deep learning approaches to educational data mining. Complexity 2019, 1–22 (2019)
Ibrahim, Z., Rusli, D.: Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In: 21st Annual SAS Malaysia Forum, 5th September (2007)
Ingels, S.J., Curtin, T.R., Kaufman, P., Alt, M.N., Chen, X., et al.: Coming of Age in the 1990s: The Eighth-Grade Class of 1988 12 Years Later. Eric (2002)
Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Predicting students’ performance in distance learning using machine learning techniques. Appl. Artif. Intell. 18(5), 411–426 (2004)
OECD: Who and Where are the Low-Performing Students? OECD Publishing (2016)
Pejić, A., Molcer, P.S., Gulači, K.: Math proficiency prediction in computer-based international large-scale assessments using a multi-class machine learning model. In: 2021 IEEE 19th International Symposium on Intelligent Systems and Informatics (SISY), pp. 49–54. IEEE (2021)
Rastrollo-Guerrero, J.L., Gomez-Pulido, J.A., Durán-Domínguez, A.: Analyzing and predicting students’ performance by means of machine learning: a review. Appl. Sci. 10(3), 1042 (2020)
Ricci, R.: La dispersione scolastica implicita (2019)
Sultana, S., Khan, S., Abbas, M.A.: Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. Int. J. Electr. Eng. Educ. 54(2), 105–118 (2017)
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Zanellati, A., Zingaro, S.P., Gabbrielli, M. (2022). Student Low Achievement Prediction. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_76
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