Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades

Daniel Doz (), Mara Cotič and Darjo Felda
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Daniel Doz: Faculty of Education, University of Primorska, 5-6000 Koper, Slovenia
Mara Cotič: Faculty of Education, University of Primorska, 5-6000 Koper, Slovenia
Darjo Felda: Faculty of Education, University of Primorska, 5-6000 Koper, Slovenia

Mathematics, 2023, vol. 11, issue 19, 1-19

Abstract: The use of fuzzy logic to assess students’ knowledge is not a completely new concept. However, despite dealing with a large quantity of data, traditional statistical methods have typically been the preferred approach. Many studies have argued that machine learning methods could offer a viable alternative for analyzing big data. Therefore, this study presents findings from a Random Forest (RF) regression analysis to understand the influence of demographic factors on students’ achievements, i.e., teacher-given grades, students’ outcomes on the national assessment, and fuzzy grades, which were obtained as a combination of the two. RF analysis showed that demographic factors have limited predictive power for teacher-assigned grades, unlike INVALSI scores and fuzzy grades. School type, macroregion, and ESCS are influential predictors, whereas gender and origin have a lesser impact. The study highlights regional and socio-economic disparities, influencing both student outcomes and fuzzy grades, underscoring the need for equitable education. Unexpectedly, gender’s impact on achievements is minor, possibly due to gender-focused policies. Although the study acknowledges limitations, its integration of fuzzy logic and machine learning sets the foundation for future research and policy recommendations, advocating for diversified assessment approaches and data-driven policymaking.

Keywords: national assessments; fuzzy logic; random forest regression; prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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