A conversational agent for smart schooling: a case study on K-12 dropout risk assessment
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/89836 |
Resumo: | The goal of smart education is to utilize advanced technology in order to improve the teaching experience by establishing a stimulating and interactive atmosphere for learning. Conversational agents emerge as an aid for a smarter education. One of the possibilities to be explored is the building of tools that help predict and prevent student failure or dropout. This case study presents a research project that consists on the creation of a school platform for student interaction, in which a conversational agent, developed using Rasa, communicates with both the students and the class director and is able to assign a risk of academic failure, based on their answers to questionnaires scripted by a team of psychologists. XGBoost outperfomed AdaBoost, Decision Tree and Random Forest algorithms with an accuracy of 97%. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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spelling |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessmentAcademic failureConversational agentMachine learningRasaSmart educationThe goal of smart education is to utilize advanced technology in order to improve the teaching experience by establishing a stimulating and interactive atmosphere for learning. Conversational agents emerge as an aid for a smarter education. One of the possibilities to be explored is the building of tools that help predict and prevent student failure or dropout. This case study presents a research project that consists on the creation of a school platform for student interaction, in which a conversational agent, developed using Rasa, communicates with both the students and the class director and is able to assign a risk of academic failure, based on their answers to questionnaires scripted by a team of psychologists. XGBoost outperfomed AdaBoost, Decision Tree and Random Forest algorithms with an accuracy of 97%.This work is supported by: FCT - Fundação para a Ciência 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.SpringerUniversidade do MinhoMagalhães, RenataVeloso, BrunoMarcondes, Francisco SupinoLima, HenriqueDurães, DalilaNovais, Paulo2023-092023-09-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/1822/89836eng978-3-031-36956-82367-33702367-338910.1007/978-3-031-36957-5_11978-3-031-36957-5https://link.springer.com/chapter/10.1007/978-3-031-36957-5_11info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-11T05:12:24Zoai:repositorium.sdum.uminho.pt:1822/89836Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T05:12:24Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
title |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
spellingShingle |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment Magalhães, Renata Academic failure Conversational agent Machine learning Rasa Smart education |
title_short |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
title_full |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
title_fullStr |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
title_full_unstemmed |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
title_sort |
A conversational agent for smart schooling: a case study on K-12 dropout risk assessment |
author |
Magalhães, Renata |
author_facet |
Magalhães, Renata Veloso, Bruno Marcondes, Francisco Supino Lima, Henrique Durães, Dalila Novais, Paulo |
author_role |
author |
author2 |
Veloso, Bruno Marcondes, Francisco Supino Lima, Henrique Durães, Dalila Novais, Paulo |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Magalhães, Renata Veloso, Bruno Marcondes, Francisco Supino Lima, Henrique Durães, Dalila Novais, Paulo |
dc.subject.por.fl_str_mv |
Academic failure Conversational agent Machine learning Rasa Smart education |
topic |
Academic failure Conversational agent Machine learning Rasa Smart education |
description |
The goal of smart education is to utilize advanced technology in order to improve the teaching experience by establishing a stimulating and interactive atmosphere for learning. Conversational agents emerge as an aid for a smarter education. One of the possibilities to be explored is the building of tools that help predict and prevent student failure or dropout. This case study presents a research project that consists on the creation of a school platform for student interaction, in which a conversational agent, developed using Rasa, communicates with both the students and the class director and is able to assign a risk of academic failure, based on their answers to questionnaires scripted by a team of psychologists. XGBoost outperfomed AdaBoost, Decision Tree and Random Forest algorithms with an accuracy of 97%. |
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/1822/89836 |
url |
https://hdl.handle.net/1822/89836 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
978-3-031-36956-8 2367-3370 2367-3389 10.1007/978-3-031-36957-5_11 978-3-031-36957-5 https://link.springer.com/chapter/10.1007/978-3-031-36957-5_11 |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817544549961039872 |