Machine learning bias in predicting high school grades

Bibliographic Details
Main Author: Costa-Mendes, Ricardo
Publication Date: 2021
Other Authors: Cruz-Jesus, Frederico, Oliveira, Tiago, Castelli, Mauro
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/126475
Summary: Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576-597. https://doi.org/10.28991/esj-2021-01298
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spelling Machine learning bias in predicting high school gradesA knowledge perspectiveKnowledge BiasVariance DecompositionRandom ForestSupport Vector RegressionPrecision EducationAcademic AchievementGeneralSDG 4 - Quality EducationCosta-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576-597. https://doi.org/10.28991/esj-2021-01298This 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCosta-Mendes, RicardoCruz-Jesus, FredericoOliveira, TiagoCastelli, Mauro2021-10-22T03:42:57Z2021-10-012021-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article22application/pdfhttp://hdl.handle.net/10362/126475eng2610-9182PURE: 33907817https://doi.org/10.28991/esj-2021-01298info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:56:45Zoai:run.unl.pt:10362/126475Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:27:49.293980Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Machine learning bias in predicting high school grades
A knowledge perspective
title Machine learning bias in predicting high school grades
spellingShingle Machine learning bias in predicting high school grades
Costa-Mendes, Ricardo
Knowledge Bias
Variance Decomposition
Random Forest
Support Vector Regression
Precision Education
Academic Achievement
General
SDG 4 - Quality Education
title_short Machine learning bias in predicting high school grades
title_full Machine learning bias in predicting high school grades
title_fullStr Machine learning bias in predicting high school grades
title_full_unstemmed Machine learning bias in predicting high school grades
title_sort Machine learning bias in predicting high school grades
author Costa-Mendes, Ricardo
author_facet Costa-Mendes, Ricardo
Cruz-Jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
author_role author
author2 Cruz-Jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
author2_role author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Costa-Mendes, Ricardo
Cruz-Jesus, Frederico
Oliveira, Tiago
Castelli, Mauro
dc.subject.por.fl_str_mv Knowledge Bias
Variance Decomposition
Random Forest
Support Vector Regression
Precision Education
Academic Achievement
General
SDG 4 - Quality Education
topic Knowledge Bias
Variance Decomposition
Random Forest
Support Vector Regression
Precision Education
Academic Achievement
General
SDG 4 - Quality Education
description Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2021). Machine learning bias in predicting high school grades: A knowledge perspective. Emerging Science Journal, 5(5), 576-597. https://doi.org/10.28991/esj-2021-01298
publishDate 2021
dc.date.none.fl_str_mv 2021-10-22T03:42:57Z
2021-10-01
2021-10-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/126475
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2610-9182
PURE: 33907817
https://doi.org/10.28991/esj-2021-01298
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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repository.mail.fl_str_mv info@rcaap.pt
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