Academic achievement critical factors and the bias and variance decomposition

Bibliographic Details
Main Author: Costa-Mendes, Ricardo
Publication Date: 2022
Other Authors: Cruz-Jesus, Frederico, Oliveira, Tiago, Castelli, Mauro
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/145605
Summary: Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2022). Academic achievement critical factors and the bias and variance decomposition: evidence from high school students’ grades. In Papers of 6th Canadian International Conference on Advances in Education, Teaching & Technology 2022: Papers proceedings (pp. 54-62). (International Multidisciplinary Research Journal; Vol. Special Issue, No. Conferences - Proceedings). Unique Conferences Canada. https://imrjournal.info/2022/EduTeach2022Proceedings1.pdf
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spelling Academic achievement critical factors and the bias and variance decompositionevidence from high school students’ gradesBias and variance decompositionEducation policyAcademic achievementSDG 4 - Quality EducationSDG 10 - Reduced InequalitiesCosta-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2022). Academic achievement critical factors and the bias and variance decomposition: evidence from high school students’ grades. In Papers of 6th Canadian International Conference on Advances in Education, Teaching & Technology 2022: Papers proceedings (pp. 54-62). (International Multidisciplinary Research Journal; Vol. Special Issue, No. Conferences - Proceedings). Unique Conferences Canada. https://imrjournal.info/2022/EduTeach2022Proceedings1.pdfThis study is centered on the sources of machine learning bias in the prediction of students’ grades. The dataset comprises 29,788 Portuguese high school teacher final grades corresponding to 10,364 public high school students’ academic paths (from the 10th to the 11th grades). We use an artificial neural network to perform the tasks. In the experimental phase, we undertake a bias and variance decomposition when predicting the 11th year students’ grades. Two different implementations are used, a critical implementation that comprises only academic achievement critical factors and a lagged implementation where the preceding teacher grade is appended. The critical implementation has a higher machine learning bias, notwithstanding the higher critical factors’ contribution. The lagged implementation, on the other hand, has a smaller bias, but a smaller critical factors’ contribution. It is possible for a machine learning model to have a reduced bias and simultaneously a little critical factors’ contribution, simply by accessing information about the historical value of the target variable. The education stakeholders should therefore be aware of the critical quality of the model in use. In defining policies and choosing the variables to influence, predictive models with low biases and built upon the critical factors information are indispensable. A machine learning model based on the critical factors produces more consistent estimates of their effects on AA. They are therefore suitable models to assist in policymaking. On the other hand, if the goal is to obtain a simple set of predictions, the use of target variable historical values is appropriate.Unique Conferences CanadaNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCosta-Mendes, RicardoCruz-Jesus, FredericoOliveira, TiagoCastelli, Mauro2022-11-17T22:17:02Z2022-09-012022-09-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion9application/pdfhttp://hdl.handle.net/10362/145605eng978-1-988652-51-12424-7073PURE: 47787815info: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:RCAAP2024-05-22T18:06:46Zoai:run.unl.pt:10362/145605Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:37:15.117800Repositórios 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 Academic achievement critical factors and the bias and variance decomposition
evidence from high school students’ grades
title Academic achievement critical factors and the bias and variance decomposition
spellingShingle Academic achievement critical factors and the bias and variance decomposition
Costa-Mendes, Ricardo
Bias and variance decomposition
Education policy
Academic achievement
SDG 4 - Quality Education
SDG 10 - Reduced Inequalities
title_short Academic achievement critical factors and the bias and variance decomposition
title_full Academic achievement critical factors and the bias and variance decomposition
title_fullStr Academic achievement critical factors and the bias and variance decomposition
title_full_unstemmed Academic achievement critical factors and the bias and variance decomposition
title_sort Academic achievement critical factors and the bias and variance decomposition
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 Bias and variance decomposition
Education policy
Academic achievement
SDG 4 - Quality Education
SDG 10 - Reduced Inequalities
topic Bias and variance decomposition
Education policy
Academic achievement
SDG 4 - Quality Education
SDG 10 - Reduced Inequalities
description Costa-Mendes, R., Cruz-Jesus, F., Oliveira, T., & Castelli, M. (2022). Academic achievement critical factors and the bias and variance decomposition: evidence from high school students’ grades. In Papers of 6th Canadian International Conference on Advances in Education, Teaching & Technology 2022: Papers proceedings (pp. 54-62). (International Multidisciplinary Research Journal; Vol. Special Issue, No. Conferences - Proceedings). Unique Conferences Canada. https://imrjournal.info/2022/EduTeach2022Proceedings1.pdf
publishDate 2022
dc.date.none.fl_str_mv 2022-11-17T22:17:02Z
2022-09-01
2022-09-01T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/145605
url http://hdl.handle.net/10362/145605
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-988652-51-1
2424-7073
PURE: 47787815
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 9
application/pdf
dc.publisher.none.fl_str_mv Unique Conferences Canada
publisher.none.fl_str_mv Unique Conferences Canada
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
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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