Predicting bootcamp success

Detalhes bibliográficos
Autor(a) principal: Santos, Ricardo
Data de Publicação: 2024
Outros Autores: Pesovski, Ivica, Henriques, Roberto, Trajkovik, Vladimir
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/170078
Resumo: Santos, R., Pesovski, I., Henriques, R., & Trajkovik, V. (2024). Predicting bootcamp success: using regression to leverage preparatory course data for tech career transitions. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference on Education and New Learning Technologies 1-3 July, 2024 Palma, Spain (pp. 6181-6190). (EDULEARN24 Proceedings; No. 2024). IATED Academy. https://doi.org/10.21125/edulearn.2024.1465
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spelling Predicting bootcamp successusing regression to leverage preparatory course data for tech career transitionsBootcampsTech EducationPredictive ModelingPreparatory CoursesCareer TransitionSDG 4 - Quality EducationSantos, R., Pesovski, I., Henriques, R., & Trajkovik, V. (2024). Predicting bootcamp success: using regression to leverage preparatory course data for tech career transitions. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference on Education and New Learning Technologies 1-3 July, 2024 Palma, Spain (pp. 6181-6190). (EDULEARN24 Proceedings; No. 2024). IATED Academy. https://doi.org/10.21125/edulearn.2024.1465In our ever-evolving digital landscape, the demand for tech-savvy professionals is soaring. However, traditional education often falls short in equipping individuals with the practical skills needed by employers. Aspiring tech enthusiasts face a dilemma: they want to gain swift entry into the industry without committing to lengthy degree programs. Meanwhile, career changers seek streamlined paths to acquire relevant skills. Programming bootcamps provide a pragmatic solution. These intensive, short-term programs prioritize hands-on learning over theoretical depth. Participants emerge with coding abilities, web application development skills, and collaborative prowess — all within months. Bootcamps attract both young learners exploring alternatives to Bachelor's degrees and professionals switching careers to tech jobs. By bridging the education-employment gap, bootcamps empower individuals for junior roles in the tech sector. However, bootcamps also pose challenges. Many participants lack formal programming training, which can impact their bootcamp success. Institutions offering these programs are incentivized to create preparatory courses, ensuring fundamental skills and providing support mechanisms. In this study, we analyze the efforts of future boot campers in preparatory courses at a European university, using leave-one-out cross-validation on a dataset of 207 bootcampers to create a predictive regression model that uses information provided upon registration and their respective attendance at the preparatory courses. Then, we used this model to predict the final score of a new cohort of 58 students and measure the model's performance by measuring the mean, squared, and root mean squared errors on the test set. In the second step, we analyzed the importance of the variables used by the predictive model by measuring the R2 score and the relative tree-based feature importance for each variable. Our results show that data collected before the start of a bootcamp can be used to predict the success of a bootcamper as our Random Forest model predicted each participant's final grade with a mean absolute error of 17.67 points (grades vary between and 100). Moreover, our model explains 46% of the final grades' variability, with prior knowledge of the topic, level of instruction and the number of completed preparatory steps among the most relevant features. Implications for both research and practice are analyzed and discussed.IATED AcademyNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSantos, RicardoPesovski, IvicaHenriques, RobertoTrajkovik, Vladimir2024-07-26T22:25:02Z2024-072024-07-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion10application/pdfhttp://hdl.handle.net/10362/170078eng978-84-09-62938-12340-1117PURE: 95959337https://doi.org/10.21125/edulearn.2024.1465info: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:RCAAP2024-08-05T01:35:49Zoai:run.unl.pt:10362/170078Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:47:00.077154Repositórios 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 Predicting bootcamp success
using regression to leverage preparatory course data for tech career transitions
title Predicting bootcamp success
spellingShingle Predicting bootcamp success
Santos, Ricardo
Bootcamps
Tech Education
Predictive Modeling
Preparatory Courses
Career Transition
SDG 4 - Quality Education
title_short Predicting bootcamp success
title_full Predicting bootcamp success
title_fullStr Predicting bootcamp success
title_full_unstemmed Predicting bootcamp success
title_sort Predicting bootcamp success
author Santos, Ricardo
author_facet Santos, Ricardo
Pesovski, Ivica
Henriques, Roberto
Trajkovik, Vladimir
author_role author
author2 Pesovski, Ivica
Henriques, Roberto
Trajkovik, Vladimir
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 Santos, Ricardo
Pesovski, Ivica
Henriques, Roberto
Trajkovik, Vladimir
dc.subject.por.fl_str_mv Bootcamps
Tech Education
Predictive Modeling
Preparatory Courses
Career Transition
SDG 4 - Quality Education
topic Bootcamps
Tech Education
Predictive Modeling
Preparatory Courses
Career Transition
SDG 4 - Quality Education
description Santos, R., Pesovski, I., Henriques, R., & Trajkovik, V. (2024). Predicting bootcamp success: using regression to leverage preparatory course data for tech career transitions. In L. G. Chova, C. G. Martínez, & J. Lees (Eds.), 16th International Conference on Education and New Learning Technologies 1-3 July, 2024 Palma, Spain (pp. 6181-6190). (EDULEARN24 Proceedings; No. 2024). IATED Academy. https://doi.org/10.21125/edulearn.2024.1465
publishDate 2024
dc.date.none.fl_str_mv 2024-07-26T22:25:02Z
2024-07
2024-07-01T00:00:00Z
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/10362/170078
url http://hdl.handle.net/10362/170078
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-84-09-62938-1
2340-1117
PURE: 95959337
https://doi.org/10.21125/edulearn.2024.1465
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 10
application/pdf
dc.publisher.none.fl_str_mv IATED Academy
publisher.none.fl_str_mv IATED Academy
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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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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