A conversational agent for smart schooling: a case study on K-12 dropout risk assessment

Detalhes bibliográficos
Autor(a) principal: Magalhães, Renata
Data de Publicação: 2023
Outros Autores: Veloso, Bruno, Marcondes, Francisco Supino, Lima, Henrique, Durães, Dalila, Novais, Paulo
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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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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection 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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