OntoLearn: A Formal Ontology for Competency-Based Learning Analytics and Semantic Modeling
Resumo
A implementação do Ensino Baseado em Competências (EBC) carece de granularidade semântica para Learning Analytics. Apresentamos a OntoLearn, uma ontologia OWL 2 DL para modelar a tríade CHA (Conhecimentos, Habilidades e Atitudes). O desenvolvimento integra a metodologia 101 ao framework ágil SAMOD. A validação utiliza uma abordagem híbrida: auditoria estrutural com OOPS!, checagem de consistência com HermiT e testes funcionais via IA Generativa (Gemini 3) sob o paradigma human-in-the-loop. Um estudo de caso com dados sintéticos confirma 100% de consistência lógica e cobertura funcional plena das oito perguntas de competência após um ciclo iterativo de refinamento, com axiomas avaliados em 4.8/5 quanto à plausibilidade semântica.
Palavras-chave:
Ontologia, Learning Analytics, Aprendizagem Baseada em Competências
Referências
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Idrissi, M., Hnida, M., and Bennani, S. (2016). Competency-Based Assessment: From Conceptual Model to Operational Tool, page 472. DOI: 10.4018/978-1-7998-2517-3.ch006.
IEEE (2007). Unapproved draft standard for learning technology - data model for reusable competency definitions. Technical report, IEEE Unapproved Draft Std P1484.20.1/D5. pages.
Monfardini, G. K. Q., Salamon, J. S., and Barcellos, M. P. (2023). Use of competency questions in ontology engineering: A survey. In International Conference on Conceptual Modeling (ER 2023), pages 45–64. Springer. DOI: 10.1007/978-3-031-47262-6_22.
Noy, N., McGuinness, D., et al. (2001). Ontology development 101: A guide to creating your first ontology. Technical report.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88:105906. DOI: 10.1016/j.ijsu.2021.105906.
Peroni, S. (2017). A simplified agile methodology for ontology development. In Dragoni, M., Poveda-Villalón, M., and Jimenez-Ruiz, E., editors, OWL: Experiences and Directions – Reasoner Evaluation, pages 55–69, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-54627-8_5.
Raad, J. and Cruz, C. (2015). A survey on ontology evaluation methods. Setubal, PRT. SCITEPRESS - Science and Technology Publications, Lda. DOI: 10.5220/0005591001790186.
Sampson, D. and Fytros, D. (2008). Competence Models in Technology-Enhanced Competence-Based Learning, pages 155–177. Springer Berlin Heidelberg, Berlin, Heidelberg. DOI: 10.1007/978-3-540-74155-8_9.
Susnjak, T., Ramaswami, S., and Mathrani, A. (2022). Learning analytics: A systematic review of the literature. International Journal of Educational Technology in Higher Education, 19(1):1–33.
World Economic Forum (2023). The future of jobs report 2023. Technical report, World Economic Forum. Available at: [link]. Accessed: Jan. 2, 2026.
Idrissi, M., Hnida, M., and Bennani, S. (2016). Competency-Based Assessment: From Conceptual Model to Operational Tool, page 472. DOI: 10.4018/978-1-7998-2517-3.ch006.
IEEE (2007). Unapproved draft standard for learning technology - data model for reusable competency definitions. Technical report, IEEE Unapproved Draft Std P1484.20.1/D5. pages.
Monfardini, G. K. Q., Salamon, J. S., and Barcellos, M. P. (2023). Use of competency questions in ontology engineering: A survey. In International Conference on Conceptual Modeling (ER 2023), pages 45–64. Springer. DOI: 10.1007/978-3-031-47262-6_22.
Noy, N., McGuinness, D., et al. (2001). Ontology development 101: A guide to creating your first ontology. Technical report.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88:105906. DOI: 10.1016/j.ijsu.2021.105906.
Peroni, S. (2017). A simplified agile methodology for ontology development. In Dragoni, M., Poveda-Villalón, M., and Jimenez-Ruiz, E., editors, OWL: Experiences and Directions – Reasoner Evaluation, pages 55–69, Cham. Springer International Publishing. DOI: 10.1007/978-3-319-54627-8_5.
Raad, J. and Cruz, C. (2015). A survey on ontology evaluation methods. Setubal, PRT. SCITEPRESS - Science and Technology Publications, Lda. DOI: 10.5220/0005591001790186.
Sampson, D. and Fytros, D. (2008). Competence Models in Technology-Enhanced Competence-Based Learning, pages 155–177. Springer Berlin Heidelberg, Berlin, Heidelberg. DOI: 10.1007/978-3-540-74155-8_9.
Susnjak, T., Ramaswami, S., and Mathrani, A. (2022). Learning analytics: A systematic review of the literature. International Journal of Educational Technology in Higher Education, 19(1):1–33.
World Economic Forum (2023). The future of jobs report 2023. Technical report, World Economic Forum. Available at: [link]. Accessed: Jan. 2, 2026.
Publicado
05/10/2026
Como Citar
NOGUEIRA, Tiago J. D. D.; LIMA, Ricardo M. F.; NOGUEIRA, Júlia C. A..
OntoLearn: A Formal Ontology for Competency-Based Learning Analytics and Semantic Modeling. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 37. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 44-56.
DOI: https://doi.org/10.5753/sbie.2026.25639.
