Deep Learning in Predicting High School Grades: A Quantum Space of Representation

Ricardo Costa-Mendes, Frederico Cruz-Jesus, Tiago Oliveira, Mauro Castelli


This paper applies deep learning to the prediction of Portuguese high school grades. A deep multilayer perceptron and a multiple linear regression implementation are undertaken. The objective is to demonstrate the adequacy of deep learning as a quantitative explanatory paradigm when compared with the classical econometrics approach. The results encompass point predictions, prediction intervals, variable gradients, and the impact of an increase in the class size on grades. Deep learning’s generalization error is lower in the student grade prediction, and its prediction intervals are more accurate. The deep multilayer perceptron gradient empirical distributions largely align with the regression coefficient estimates, indicating a satisfactory regression fit. Based on gradient discrepancies, a student’s mother being an employer does not seem to be a positive factor. A benign paradigm shift concerning the balance between home and career affairs for both genders should be reinforced. The deep multilayer perceptron broadens the spectrum of possibilities, providing a quantum solution hinged on a universal approximator. In the case of an academic achievement-critical factor such as class size, where the literature is neither unanimous on its importance nor its direction, the multilayer perceptron formed three distinct clusters per the individual gradient signals.


Doi: 10.28991/ESJ-2022-SIED-012

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Data Science Applications in Education; Secondary Education; Social Sciences; Deep Learning; Artificial Neural Networks; Academic Achievement.


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DOI: 10.28991/ESJ-2022-SIED-012


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