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Ricardo Costa-Mendes

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa,

Author-ID : 2632436

Environmental Science

Published : 2 Documents Claim Missing Document

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Machine Learning Bias in Predicting High School Grades: A Knowledge Perspective Ricardo Costa-Mendes; Frederico Cruz-Jesus; Tiago Oliveira; Mauro Castelli
Emerging Science Journal Vol 5, No 5 (2021): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-01298

This study focuses on the machine learning bias when predicting teacher grades. The experimental phase consists of predicting the student grades of 11th and 12thgrade Portuguese high school grades and computing the bias and variance decomposition. In the base implementation, only the academic achievement critical factors are considered. In the second implementation, the preceding year’s grade is appended as an input variable. The machine learning algorithms in use are random forest, support vector machine, and extreme boosting machine. The reasons behind the poor performance of the machine learning algorithms are either the input space poor preciseness or the lack of a sound record of student performance. We introduce the new concept of knowledge bias and a new predictive model classification. Precision education would reduce bias by providing low-bias intensive-knowledge models. To avoid bias, it is not necessary to add knowledge to the input space. Low-bias extensive-knowledge models are achievable simply by appending the student’s earlier performance record to the model. The low-bias intensive-knowledge learning models promoted by precision education are suited to designing new policies and actions toward academic attainments. If the aim is solely prediction, deciding for a low bias knowledge-extensive model can be appropriate and correct. Doi: 10.28991/esj-2021-01298 Full Text: PDF

Deep Learning in Predicting High School Grades: A Quantum Space of Representation Ricardo Costa-Mendes; Frederico Cruz-Jesus; Tiago Oliveira; Mauro Castelli
Emerging Science Journal Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-012

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 Full Text: PDF

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Journal : Emerging Science Journal