ATHENA: Un modelo computacional para servicios inteligentes en educación a distancia utilizando historiales de contexto
DOI:
https://doi.org/10.5753/rbie.2026.5487Keywords:
Servicios inteligentes, Entorno virtual de aprendizaje, Contexto, Historia del contexto, Educación a distanciaAbstract
La Educación a Distancia (EaD) ha ganado protagonismo en la expansión y democratización de la educación superior, beneficiándose de las Nuevas Tecnologías de la Información y las Comunicaciones (NTIC) y de los cambios en los procesos de enseñanza y aprendizaje. Esta evasión no se limita a la educación a distancia, sino que también afecta a la enseñanza presencial y semipresencial, en instituciones públicas y privadas. Ante este escenario, el artículo presenta Athena, un modelo computacional diseñado para ofrecer servicios inteligentes en educación a distancia basados en las historias contextuales de los estudiantes. Athena tiene como objetivo ayudar a directivos y profesores con la planificación estratégica y el seguimiento del progreso académico de los estudiantes. El modelo ofrece servicios para predecir el rendimiento académico de los estudiantes y formar grupos de estudio. Estos servicios tienen como objetivo mejorar el proceso de aprendizaje, reducir la tasa de fracaso y, en consecuencia, reducir las tasas de abandono. Athena utiliza una ontología para representar el conocimiento del aprendizaje a distancia y analiza las historias contextuales de los estudiantes para personalizar los servicios ofrecidos. Un prototipo fue probado con datos de 25 estudiantes del curso de Tecnología en Análisis y Desarrollo de Sistemas. Las pruebas evaluaron dos servicios: Forecasting, que anticipa el rendimiento académico, y Grouping, que organiza grupos de estudio. Los resultados confirman la viabilidad del modelo para apoyar la creación y el uso de diferentes servicios inteligentes en la educación a distancia, basados en las historias contextuales de los estudiantes.
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Allmendinger, G., Lombreglia, R. (2005). Four strategies for the age of smart services. Harvard Business Review. ISSN:00178012, 83(10), 131-145. [GS Search]
Almeida Maia, M. D. S. de, Silva, D. G. (2020). Práticas pedagógicas em ambientes virtuais de aprendizagem: usos e abusos. EmRede - Revista de Educação a Distância, [S.l.], 7(1), 81–95. https://doi.org/10.53628/emrede.v7i1.555
Almeida Neto, F. A. D. (2017). Um Framework para Criação de Serviços Inteligentes em Ambientes Flexíveis na Web. Universidade Federal do Amazonas (216 páginas). Disponível em: [GS Search]
Andrade, T. L. de; Rigo, S. J.; Barbosa, J. L. V. (2022). Active Methodology, Educational Data Mining and Learning Analytics: a systematic mapping study. Informatics in Education. 20 no. 2, 171-204, DOI: [GS Search]
Angeline, D. M. D., Ramasubramanian, P., James, I. S. P. (2020). The Discriminant Analysis Approach for Evaluating Effectiveness of Learning in an Instructor-Led Virtual Classroom. International Journal on Smart Sensing and Intelligent Systems, Sydney, 13(1), 1–15. DOI: [GS Search]
Anjos, R. A. V. (2015). Referencial Pedagógico Para Análise de Ambientes Virtuais de Aprendizagem. Disponível em: http://ri.ufmt.br/handle/1/74
Aranda, J.A.S., Bavaresco, R.S., de Carvalho, J.V. et al. A computational model for adaptive recording of vital signs through context histories. J Ambient Intell Human Comput 14, 16047–16061 (2023). [GS Search]
Bertiz, Y., Kocaman Karoglu, A. (2020). Distance Education Students Cognitive Flexibility Levels and Distance Education Motivations. International Journal of Research in Education and Science (IJRES), 6(4), 638-648. [GS Search]
Beverungen, D., Müller, O., Matzner, M. et al. (2019) Conceptualizing smart service systems. Electron Markets 29, 7–18. DOI: https://doi.org/10.1007/s12525-017-0270-5
Bezerra, C., Freitas, F., Santana da Silva, F. (2013). CQChecker: A Tool to Check the Satisfaction of Description Logic Competency Questions on Ontologies. Conference: X Encontro Nacional de Inteligência Artificial e Computacional (ENIAC). At: Fortaleza. Disponível em: [GS Search]
Campos, A., Hollerweger, L., Santos, G., Farias, A. F., Behar, P. A. (2017) Mapeamento de Soluções tecnológicas em Sistemas de Recomendação Educacionais em âmbito brasileiro. Informática na Educação: teoria & prática, Porto Alegre, 20(3), 79-96. [GS Search]
Chen, H., Yin, C., Li, R., Rong, W., Xiong, Z., David, B. (2020). "Enhanced learning resource recommendation based on online learning style model," in Tsinghua Science and Technology, vol. 25, no. 3, pp. 348-356, June 2020. DOI: [GS Search]
Chen, Y., Zheng, Q., Ji, S., Tian, F., Zhu, H., Liu, M. (2020). Identifying at-risk students based on the phased prediction model. Knowledge and Information Systems, 62, 987-1003. [GS Search]
Costa, M. A. B., Guedes, P. da S., & Guerra, R. S. (2021). Desafios da educação a distância on-line. Revista Ibero-Americana De Humanidades, Ciências e Educação, 7(9), 766–776. https://doi.org/10.51891/rease.v7i9.2279
Cummaudo, A., Vasa, R., Grundy, J., Abdelrazek, M., Cain, A. (2019). Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services, 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), Cleveland, OH, USA, 333-342. [GS Search]
Dias, R. S. (2017) Caracterização do learning analytics na educação a distância. Anais do Seminário de Pesquisa e Inovação Tecnológica - SEPIT, [S. l.], 1(1). Disponível em: https://periodicos.iftm.edu.br/index.php/sepit/article/view/312
El Fouki, M., Aknin, N., El. KadirI, K. E. (2017). Intelligent Adapted eLearning System based on Deep Reinforcement Learning. In Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems (ICCWCS’17). Association for Computing Machinery, New York, NY, USA, 85, 1–6. [GS Search]
Flach, L. (2022) Uso de algoritmos de machine learning para prever a evasão escolar no ensino superior: um estudo no Instituto Federal de Santa Catarina. Dissertação (mestrado profissional) - Universidade Federal de Santa Catarina, Centro Sócio-Econômico, Programa de Pós-Graduação em Administração Universitária, Florianópolis. https://repositorio.ufsc.br/bitstream/handle/123456789/238320/PPAU0264-D.pdf?sequence=-1
Filippetto, A. S.; Lima, R.; Barbosa, J. L. V. (2021). A risk prediction model for software project management based on similarity analysis of context histories. Information and Software Technology, v. 131, p. 106497, 2021. Issn: 0950-5849, DOI: [GS Search]
Freitas, F. A. da S., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Dewan, M. A. A., Albuquerque, V. H. C. de, Filho, P. P. R. (2020). IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data. Electronics, 9(10):1613. [GS Search]
Garcia Martins, M.; Nesi, L.; Silva Pereira, P. R. D.; Barbosa, J. L. V. (2021). DELFOS: A Model for Multitemporal Analysis based on Contexts History, in IEEE Latin America Transactions, vol. 19, no. 9, pp. 1478-1485, Sept. 2021, DOI: [GS Search]
Hashim, A; Akeel, W. Khalaf, A. (2020). Student Performance Prediction Model based on Supervised Machine Learning Algorithms. IOP Conference Series: Materials Science and Engineering. 928:032019. [GS Search]
Heckler, W. F.; De Carvalho, J. V.; Barbosa, J. L. V. (2020). Machine learning for suicidal ideation identification: a systematic literature review. Computers in Human Behavior, v. 128, p. 107095, 2022. issn: 0747-5632. DOI: https://doi.org/10.1016/j.chb.2021.107095
Heckler, W. F.; De Carvalho, J. V.; Barbosa, J. L. V. (2020). Machine learning for suicidal ideation identification: a systematic literature review. Computers in Human Behavior, v. 128, p. 107095, 2022. issn: 0747-5632. DOI: [GS Search]
Heckler, W. F; Feijó, L.P.; Carvalho, J. C.; Barbosa, L. J. V. (2023). Thoth: An intelligent model for assisting individuals with suicidal ideation, Expert Systems with Applications, Volume 233, 120918, ISSN 0957-4174, DOI: [GS Search]
Kim, W., Kim, J. (2020). Individualized AI Tutor Based on Developmental Learning Networks, in IEEE Access, 8, 27927-27937. [GS Search]
Koldewey, C., Gausemeier, J., Dumitrescu, R., Evers, H.H., Frank, M., Reinhold, J. (2021). Development Process for Smart Service Strategies: Grasping the Potentials of Digitalization for Servitization. In: Schallmo, D.R.A., Tidd, J. (eds) Digitalization. Management for Professionals. Springer, Cham. [GS Search]
Koldewey, C., Meyer, M., Stockbrügger, P., Dumitrescu, R., Gausemeier, J. (2020). Framework and Functionality Patterns for Smart Service Innovation, Procedia CIRP, ISSN 2212-8271, 91, 851-857. [GS Search]
Larentis A. V. Salus: um modelo para assistência educacional ubíqua em doenças crônicas não transmissíveis. Tese. Universidade do Vale do Rio dos Sinos – UNISINOS. São Leopoldo, Rs. 2022. https://repositorio.jesuita.org.br/
Larentis, A. V.; Neto, E. G. D. A.; Barbosa, J. L. V.; Barbosa, D. N. F.; Leithardt, V. R. Q.; Correia, S. D. (2021). Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases. Computers 2021, 10, 128. DOI: [GS Search]
Lima, R.; Filippetto, A. S.; Heckler, W.; Barbosa, J. L. V.; Leithardt, V. R. Q. (2022). Towards ubiquitous requirements engineering through recommendations based on context histories. PeerJ Computer Science 8: e794. DOI: [GS Search]
Machado, S. D.; Tavares, J. E. D. R.; Martins, M. G.; Barbosa, J. L. V.; González, G. V.; Leithardt, V. R. Q. (2021). Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories. Electronics 2021, 10, 1260. DOI: https://doi.org/10.3390/electronics10111260
Martini, B. G.; Helfer, G. A.; Barbosa, J. L. V.; Espinosa Modolo, R. C.; Silva, M. R. Figueiredo, R. M. De; Mendes, A. S.; Silva, L. A.; Leithardt, V. R. Q. (2021.) Indoorplant: A model for inteligente services in indoor agriculture based on context histories. Sensors (Basel, Switzerland) 21(5):1631, DOI: https://doi.org/10.3390/s21051631
Noy, N. & Mcguinness, D. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. Knowledge Systems Laboratory. 32. [GS Search]
Rodrigues, F. C.; Filippetto, A. S.; Lima, R. K. D.; Heckler, W. F.; Barbosa, J. L. V. (2022). Kairós: using context histories for predictions and recommendations in projects time management. International Journal of Agile Systems and Management (IJASM), Vol. 15, No. 1, 2022. DOI: https://doi.org/10.1504/IJASM.2022.124168
Vielitz, F. L.; Martins, M. G.; Barbosa, J. L. V.; Oliveira, K. F. De; Dias, L. P. S.; Wolf, A. (2019). CMFRAME: a Framework for Managing Dynamic and Hierarchical Context Histories, 2019 XLV Latin American Computing Conference (CLEI), Panama, Panama, 2019, pp. 1-8, DOI: https://doi.org/10.1109/CLEI47609.2019.235107
Web Protégé. Disponível em: https://protege.stanford.edu/
Wiedemann, T.; Barbosa, J.; Matter, V.; Gonçales, L.; Nesi, L.; Farias, K.; Rigo, S. SIMCOP: (2021). SIMCOP: a framework for similarity analysis of context histories. International Journal of Business Information Systems, v. 1, p. 1-29, 2021. DOI: https://doi.org/10.1504/IJBIS.2020.10036955
Zaoudi, M., Belhadaoui, H. (2020) Adaptive e-learning: Adaptation of content according to the continuous evolution of the learner during his training. In: Proceedings of the 3rd International Conference on Networking, Information Systems and Security, Association for Computing Machinery, New York, NY, USA, NISS2020. https://doi.org/10.1145/3386723.3387890
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Derechos de autor 2026 Lidia Martins da Silva, Andrêsa Vargas Larentis, Sandro José Rigo , Jorge Luis Victória Barbosa
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