Prediction of students’ grades based on non-academic data

Bibliographic Details
Main Author: Lacerda, Beatriz
Publication Date: 2023
Other Authors: Marcondes, Francisco Supino, Lima, Henrique, Durães, Dalila, Novais, Paulo
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/1822/89840
Summary: This study examines the use of machine learning techniques to predict Math and Portuguese grades based on student demographics and survey data regarding their school experiences. Using a sample of 53 middle school students, an accuracy rate of 93% was achieved with a support vector machine model. This paper’s findings suggest that non-academic factors such as school climate and student engagement can have a significant impact on academic performance.
id RCAP_3d80c6a90e78aab1d91bc3c55789fead
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/89840
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Prediction of students’ grades based on non-academic dataAcademic performanceEducational data miningMachine learningThis study examines the use of machine learning techniques to predict Math and Portuguese grades based on student demographics and survey data regarding their school experiences. Using a sample of 53 middle school students, an accuracy rate of 93% was achieved with a support vector machine model. This paper’s findings suggest that non-academic factors such as school climate and student engagement can have a significant impact on academic performance.This work is supported by: FCT - Fundação para a Ciên cia e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE- 01-0247-FEDER-047004 and by FCT– Fundação para a Ciência e Tecnologia within the R&D Units Project Scope:UIDB/00319/2020.SpringerUniversidade do MinhoLacerda, BeatrizMarcondes, Francisco SupinoLima, HenriqueDurães, DalilaNovais, Paulo2023-092023-09-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/89840eng978-3-031-41225-72367-33702367-338910.1007/978-3-031-41226-4_9978-3-031-41226-4https://link.springer.com/chapter/10.1007/978-3-031-41226-4_9info: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:RCAAP2024-05-11T06:06:18Zoai:repositorium.sdum.uminho.pt:1822/89840Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:41:03.807936Repositórios 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 Prediction of students’ grades based on non-academic data
title Prediction of students’ grades based on non-academic data
spellingShingle Prediction of students’ grades based on non-academic data
Lacerda, Beatriz
Academic performance
Educational data mining
Machine learning
title_short Prediction of students’ grades based on non-academic data
title_full Prediction of students’ grades based on non-academic data
title_fullStr Prediction of students’ grades based on non-academic data
title_full_unstemmed Prediction of students’ grades based on non-academic data
title_sort Prediction of students’ grades based on non-academic data
author Lacerda, Beatriz
author_facet Lacerda, Beatriz
Marcondes, Francisco Supino
Lima, Henrique
Durães, Dalila
Novais, Paulo
author_role author
author2 Marcondes, Francisco Supino
Lima, Henrique
Durães, Dalila
Novais, Paulo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Lacerda, Beatriz
Marcondes, Francisco Supino
Lima, Henrique
Durães, Dalila
Novais, Paulo
dc.subject.por.fl_str_mv Academic performance
Educational data mining
Machine learning
topic Academic performance
Educational data mining
Machine learning
description This study examines the use of machine learning techniques to predict Math and Portuguese grades based on student demographics and survey data regarding their school experiences. Using a sample of 53 middle school students, an accuracy rate of 93% was achieved with a support vector machine model. This paper’s findings suggest that non-academic factors such as school climate and student engagement can have a significant impact on academic performance.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
2023-09-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/89840
url https://hdl.handle.net/1822/89840
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-031-41225-7
2367-3370
2367-3389
10.1007/978-3-031-41226-4_9
978-3-031-41226-4
https://link.springer.com/chapter/10.1007/978-3-031-41226-4_9
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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
_version_ 1833595472705486848

Similar Items