Career Recommendation System for Validation of Multiple Intelligence to High School Students
Career choice is a critical moment for any student. However, many students worldwide change of path during the first academic year. Vocational tests are tools to help students choose an upper education career by providing information about their abilities and interests. However, some of them are ver...
- Autores:
-
Sabalza Mejia, Maryori
Campillo Jimenez, Carolina
Juan Carlos, Martínez-Santos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12301
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12301
https://doi.org/10.1007/978-3-030-86702-7_10
- Palabra clave:
- Collaborative Filtering;
Recommender Systems;
Factorization
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students Collaborative Filtering; Recommender Systems; Factorization LEMB |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Career Recommendation System for Validation of Multiple Intelligence to High School Students |
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Sabalza Mejia, Maryori Campillo Jimenez, Carolina Juan Carlos, Martínez-Santos |
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Sabalza Mejia, Maryori Campillo Jimenez, Carolina Juan Carlos, Martínez-Santos |
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Collaborative Filtering; Recommender Systems; Factorization |
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Collaborative Filtering; Recommender Systems; Factorization LEMB |
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LEMB |
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Career choice is a critical moment for any student. However, many students worldwide change of path during the first academic year. Vocational tests are tools to help students choose an upper education career by providing information about their abilities and interests. However, some of them are very difficult to apply high costs, availability, or not validated at diverse populations. Gardner’s Multiple Intelligence test is a popular test that measures the brain’s performance abilities in nine areas. Unlike IQ’s test, Gardner Test is helpful to understand students’ strengths and learning styles. Vocational tests and Multiple Intelligence test measure students’ abilities and interests. This research shows the implementation of a recommendation system based on the Gardner test, we considering familiar variables and results of a basic knowledge test. For validation, we compare it with a standard vocational test. Our system recommends a top five of careers with 88.2% success and 93.3% for the top one. © 2021, Springer Nature Switzerland AG. |
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2021 |
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2021 |
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2023-07-21T15:54:57Z |
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2023 |
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Mejia, M. S., Jimenez, C. C., & Martínez-Santos, J. C. (2021, September). Career Recommendation System for Validation of Multiple Intelligence to High School Students. In Workshop on Engineering Applications (pp. 110-120). Cham: Springer International Publishing. |
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https://hdl.handle.net/20.500.12585/12301 |
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https://doi.org/10.1007/978-3-030-86702-7_10 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Mejia, M. S., Jimenez, C. C., & Martínez-Santos, J. C. (2021, September). Career Recommendation System for Validation of Multiple Intelligence to High School Students. In Workshop on Engineering Applications (pp. 110-120). Cham: Springer International Publishing. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12301 https://doi.org/10.1007/978-3-030-86702-7_10 |
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eng |
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Sabalza Mejia, Maryoric766a58f-9892-46f0-84a7-ba645244e3e2Campillo Jimenez, Carolina67f88287-11db-43dc-8656-134faab4645dJuan Carlos, Martínez-Santosfafc3a55-6d51-476a-a487-0ceaa81bee272023-07-21T15:54:57Z2023-07-21T15:54:57Z20212023Mejia, M. S., Jimenez, C. C., & Martínez-Santos, J. C. (2021, September). Career Recommendation System for Validation of Multiple Intelligence to High School Students. In Workshop on Engineering Applications (pp. 110-120). Cham: Springer International Publishing.https://hdl.handle.net/20.500.12585/12301https://doi.org/10.1007/978-3-030-86702-7_10Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarCareer choice is a critical moment for any student. However, many students worldwide change of path during the first academic year. Vocational tests are tools to help students choose an upper education career by providing information about their abilities and interests. However, some of them are very difficult to apply high costs, availability, or not validated at diverse populations. Gardner’s Multiple Intelligence test is a popular test that measures the brain’s performance abilities in nine areas. Unlike IQ’s test, Gardner Test is helpful to understand students’ strengths and learning styles. Vocational tests and Multiple Intelligence test measure students’ abilities and interests. This research shows the implementation of a recommendation system based on the Gardner test, we considering familiar variables and results of a basic knowledge test. For validation, we compare it with a standard vocational test. Our system recommends a top five of careers with 88.2% success and 93.3% for the top one. © 2021, Springer Nature Switzerland AG.11 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Workshop on Engineering ApplicationsCareer Recommendation System for Validation of Multiple Intelligence to High School Studentsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Collaborative Filtering;Recommender Systems;FactorizationLEMBCartagena de IndiasKelly, D., Tangney, B.: Predicting learning characteristics in a multiple intelligence based tutoring system. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 678–688. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30139-4 64Shearer, C.B., Luzzo, D.A. Exploring the application of multiple intelligences theory to career counseling (2009) Career Dev Q, 58 (1), pp. 3-13. Cited 17 times.Kaewkiriya, T., Utakrit, N., Tiantong, M. The design of a rule base for an e-learning recommendation system base on multiple intelligences (2016) Int. J. Inf. Educ. Technol., 6 (3), p. 206. Cited 10 times.Obeid, C., Lahoud, I., El Khoury, H., Champin, P.-A. Ontology-based Recommender System in Higher Education (2018) The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, pp. 1031-1034. Cited 56 times. http://dl.acm.org/citation.cfm?id=3184558 ISBN: 978-145035640-4 doi: 10.1145/3184558.3191533Yadalam, T.V., Gowda, V.M., Kumar, V.S., Girish, D., Namratha, M. Career recommendation systems using Content based Filtering (2020) Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, art. no. 09137992, pp. 660-665. Cited 19 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9130794 ISBN: 978-172815371-1 doi: 10.1109/ICCES48766.2020.09137992Subramanian, E.K. Ramachandran: Student career guidance system for recommendation of relevant course selection (2019) Int. J. Recent Technol. Eng. (IJRTE), 7 (102), p. 4.Dhar, J., Jodder, A.K. An effective recommendation system to forecast the best educational program using machine learning classification algorithms (2020) Ingenierie des Systemes d'Information, 25 (5), pp. 559-568. Cited 16 times. http://www.iieta.org/journals/isi/paper/10.18280/isi.250502 doi: 10.18280/ISI.250502Gardner, H., Hatch, T. Educational Implications of the Theory of Multiple Intelligences (1989) Educational Researcher, 18 (8), pp. 4-10. Cited 316 times. doi: 10.3102/0013189X018008004Fuess, C.M. (1950) The College Board; Its First Fifty Years. Cited 22 times.Muthanna, A., Sang, G. Undergraduate Chinese students’ perspectives on Gaokao examination: Strengths, weaknesses, and implications (2015) Int. J. Res. Stud. Educ., 5 (2), pp. 3-12. Cited 19 times.Drave, N. (2017) Hong Kong Diploma of Secondary Education (HKDSE) English Language Level Descriptors: Stakeholder Recognition and Understanding. Learning and Assessment: Making the Connections, p. 64.Kwon, S.K., Lee, M., Shin, D. Educational assessment in the Republic of Korea: lights and shadows of high-stake exam-based education system (2017) Assessment in Education: Principles, Policy and Practice, 24 (1), pp. 60-77. Cited 70 times. http://www.tandfonline.com/toc/caie20/current doi: 10.1080/0969594X.2015.1074540Arnold, B.C. (2014) Pareto Distribution. Wiley Statsref: Statistics Reference Online, pp. 1-10. Cited 263 times.Chen, T., Guestrin, C. XGBoost: A scalable tree boosting system (2016) Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, pp. 785-794. Cited 17237 times. ISBN: 978-145034232-2 doi: 10.1145/2939672.2939785Song, Y., Liang, J., Lu, J., Zhao, X. An efficient instance selection algorithm for k nearest neighbor regression (2017) Neurocomputing, 251, pp. 26-34. Cited 180 times. www.elsevier.com/locate/neucom doi: 10.1016/j.neucom.2017.04.018Pontificia Universidad Católica de Perú, Introducción a la vida universitaria. https://facultad.pucp.edu.pe/generales-ciencias/servicio/introduccion-a-la-vida-universitaria/. Accessed Feb 2021Universidad Tecnológica Nacional, Introducción a la vida universitaria. http://www.frlr.utn.edu.ar/archivos/sec-academica/apunte-introduccion-vida-univ-ingreso.pdf. 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