TY - JOUR
T1 - Deep Learning in Predicting High School Grades
T2 - A Quantum Space of Representation
AU - Costa-Mendes, Ricardo
AU - Cruz-jesus, Frederico
AU - Oliveira, Tiago
AU - Castelli, Mauro
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0032%2F2018/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT#
Costa-mendes, R., Cruz-jesus, F., Oliveira, T., & Castelli, M. (2022). Deep Learning in Predicting High School Grades: A Quantum Space of Representation. Emerging Science Journal, 6, 166-187. https://doi.org/10.28991/ESJ-2022-SIED-012. Funding: This study was funded by FCT – Fundação para a Ciência e Tecnologia (DSAIPA/DS/0032/2018)
PY - 2022/9/10
Y1 - 2022/9/10
N2 - 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.
AB - 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.
KW - Data Science Applications in Education
KW - Secondary Education
KW - Social Sciences
KW - Deep Learning
KW - Artificial Neural Networks
KW - Academic Achievement
UR - http://www.scopus.com/inward/record.url?scp=85147019320&partnerID=8YFLogxK
U2 - 10.28991/ESJ-2022-SIED-012
DO - 10.28991/ESJ-2022-SIED-012
M3 - Article
SN - 2610-9182
VL - 6
SP - 166
EP - 187
JO - Emerging Science Journal
JF - Emerging Science Journal
IS - Special Issue
ER -