II. ASPIRED: ALGORITMO PROPOSTO PARA A INTEGRAÇÃO DE SEQUÊNCIAS DIDÁTICAS ADAPTATIVAS
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No campo da aprendizagem adaptativa, algumas propostas têm optado pela uso de sistemas de inferência fuzzy como método para obter a quantidade e o tipo de recursos ou objetos de aprendizagem necessários para um melhor desempenho do aluno. Porém, Estas propostas não estabelecem um método para a integração de sequências didáticas de desses recursos de aprendizagem. Esta pesquisa teve como objetivo apresentar um algoritmo para a produção de sequências didáticas adaptativas com base nos valores gerado por sistemas de inferência fuzzy usados em sistemas de aprendizagem adaptativo. A metodologia utilizada foi o desenvolvimento em cascata, o que permitiu gerar o algoritmo Sequential, Prioritized, Interleaved e Recursive Depletion para o Atribuição de Objetos de Aprendizagem (ASPIRED). Para fins de teste, este algoritmo foi traduzido para a linguagem de programação Python, onde três sequências didáticas de diferentes valores fuzzy correspondentes a quatro tipos de objetos de aprendizagem (texto, áudio, vídeo e infográficos). Como resultado, observou-se que o sequências didáticas foram geradas com baixo tempo de execução e com baixo uso de recursos de processamento e memória. Concluiu-se mencionando que o algoritmo como características ser eficaz, modular, flexível, adaptável e compreensível; igualmente, foram expostos os benefícios e possíveis aplicações do algoritmo em pesquisas futuras
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Keywords
Algoritmo, Sequência, Didática, Aprendizagem, Adaptativo
References
Afini N., Shuib L., Md Nasir H.N., Bimba A., Idris N. & Balakrishnan V. (2018).
Identification of personal traits in adaptive learning environment: Systematic
literature review, Computers & Education. 130. 168-190
https://doi.org/10.1016/j.compedu.2018.11.005
Almohammadi, K, Hagras, H., Yao, Bo., Alzahrani, A., Alghazzawi, D. y Aldabbagh, G.
(2015). A type-2 fuzzy logic recommendation system for adaptive teaching. Soft
Computing, 21(4), 965–979. https://doi.org/10.1007/s00500-015-1826-y
Balasubramanian, V. y Margret, S. (2018). Learning style detection based on cognitive skills
to support adaptive learning environment – A reinforcement approach. Ain Shams
Engineering Journal, 9, 895-907. https://doi.org/10.1016/j.asej.2016.04.012
Bradac y Walek (2017). A comprehensive adaptive system for e-learning of foreign
languages. Expert Systems with Applications. 90, 414-426.
https://doi.org/10.1016/j.eswa.2017.08.019
Collazo, Y., Paez, M. y Fernández, J. (2021). Los objetos de aprendizaje: una revisión
bibliográfica con enfoque bibliométrico. (2021). Ciencias de la Información. 52(1).
3-10. https://bit.ly/rev_bib_OA
Ennouamani, S. y Mahani, Z. (2019). Towards adaptive learning systems based on fuzzylogic. Intelligent Computing-Proceedings of the Computing Conference. 625-640.
https://doi.org/10.1007/978-3-030-22871-2_42
Guevara, C. y Aguilar, J. (2019). Modelo Ontológico del Estándar LOM Extendido para la
Gestión de Objetos de Aprendizaje Adaptativos. Avances y retos de la ciencia e
ingeniería. 315-324. https://bit.ly/3TQyR2R
Hernandez-Cardenas, L., Castano, L., Cruz-Guzman, C. y Nigenda-Alvarez, J. (2021).
Personalised learning model for academic leveling and improvement in higher
education. Australasian Journal of Educational Technology, 38(2), 70-82.
https://bit.ly/3QxTE8v
Hussain, S., Kim, Y., Thakur, S. y Breslin, J. (2022). Optimization of Waiting Time for
Electric Vehicles Using a Fuzzy Inference System. IEEE Transactions on Intelligent
Transportation Systems. 1-12. https://doi.org/10.1109/TITS.2022.3140461
Karoui, A., Alvarez, L., Goffre, T., Dherbey-Chapuis, N., Rodi, M. y Ramalho, M. (2021),
Pathways within the European Platform for Personalized Language Learning PEAPL.
Proceedings of the 29th ACM Conference on User Modeling, Adaptation and
Personalization. 90–94. https://doi.org/10.1145/3450614.3464480
López, A., González, A y Guerra, C. (2021). Desarrollo e implementación de un sistema de
inferencia difuso en un juego serio que ayude a fortalecer el razonamiento lógicomatemático. Ingenierías, 24(90), https://bit.ly/FIS_raz_log_mat
Marzal, M y Parra, P. (2021). La educación competencial desde Visual Literacy y Gaming
para la innovación educativa: propuesta para un diseño instruccional de curso.
Ibersid. 15(1), 75-83, https://bit.ly/edu_comp
Megahed, M., y Mohammed, A. (2020). Modeling Adaptive E-Learning Environment using
Facial Expressions and Fuzzy Logic. Expert Systems with Applications, 113460.
https://doi.org/10.1016/j.eswa.2020.113460
Ministerio de Educación de España. (2009). Anexo I objeto digital educativo (ODE)
definición, arquitectura, niveles de agregación y tipología. Madrid: Gobierno de
España. https://bit.ly/3qp09jl
Mohamed, F., Abdeslam, J., Lahcen, E.B. (2017). Personalization of learning activities
within a virtual environment for training based on fuzzy logic theory. Mathematics in
Natural Science. 3 (2018), 12–17 https://doi.org/10.22436/mns.03.01.02
Nafea, S., Siewe, F. y He, Y. (2018). ULEARN: Personalized Course Learning Objects Based
on Hybrid Recommendation Approach. International Journal of Information and
Education Technology, 8(12), 842-847. https://doi.org/
10.18178/ijiet.2018.8.12.1151
Núñez-Márquez, I., Rodríguez, L., Salazar, G., Castro, L., et al. (2015). A Framework for
Automatic Identification of Learning Styles in Learning Management Systems.
Research in Computing Science 106 (2015). pp. 59–68; rec. 2015-09-04; acc. 2015-
10-02. https://bit.ly/3L0gwfE
Pascual, S. (2021). Recursos Educativos Abiertos en las bibliotecas universitarias españolas:
el caso del TFG. Facultad de Filosofía y Letras de la Universidad de Zaragoza.
https://bit.ly/rec_edu_a
Quintanar, R. y Hernández, S. (2022). Modelos Tecnológicos De Aprendizaje Adaptativo
Aplicados a La Educación. Revista Tecnológica-Educativa Docentes 2.0 15 (1). 41-
66. https://doi.org/10.37843/rted.v15i1.308.
Xie, H., Chu, H., Hwang, G. & Wang, Ch. (2019). Trends and development in technologyenhanced adaptive/ personalized learning: A systematic review of journal
publications from 2007 to 2017. Computers and Education. 140 (2019).
https://doi.org/10.1016/j.compedu.2019.103599
Zhao, Q., Lai, Sh., Wang, J. y Wang, L. (2021). Hybrid Fuzzy Rule-Based Classification
System for Moodle LMS System, Journal of Internet Technology, 22(1), 81-90.
https://doi.org/10.3966/160792642021012201008
Identification of personal traits in adaptive learning environment: Systematic
literature review, Computers & Education. 130. 168-190
https://doi.org/10.1016/j.compedu.2018.11.005
Almohammadi, K, Hagras, H., Yao, Bo., Alzahrani, A., Alghazzawi, D. y Aldabbagh, G.
(2015). A type-2 fuzzy logic recommendation system for adaptive teaching. Soft
Computing, 21(4), 965–979. https://doi.org/10.1007/s00500-015-1826-y
Balasubramanian, V. y Margret, S. (2018). Learning style detection based on cognitive skills
to support adaptive learning environment – A reinforcement approach. Ain Shams
Engineering Journal, 9, 895-907. https://doi.org/10.1016/j.asej.2016.04.012
Bradac y Walek (2017). A comprehensive adaptive system for e-learning of foreign
languages. Expert Systems with Applications. 90, 414-426.
https://doi.org/10.1016/j.eswa.2017.08.019
Collazo, Y., Paez, M. y Fernández, J. (2021). Los objetos de aprendizaje: una revisión
bibliográfica con enfoque bibliométrico. (2021). Ciencias de la Información. 52(1).
3-10. https://bit.ly/rev_bib_OA
Ennouamani, S. y Mahani, Z. (2019). Towards adaptive learning systems based on fuzzylogic. Intelligent Computing-Proceedings of the Computing Conference. 625-640.
https://doi.org/10.1007/978-3-030-22871-2_42
Guevara, C. y Aguilar, J. (2019). Modelo Ontológico del Estándar LOM Extendido para la
Gestión de Objetos de Aprendizaje Adaptativos. Avances y retos de la ciencia e
ingeniería. 315-324. https://bit.ly/3TQyR2R
Hernandez-Cardenas, L., Castano, L., Cruz-Guzman, C. y Nigenda-Alvarez, J. (2021).
Personalised learning model for academic leveling and improvement in higher
education. Australasian Journal of Educational Technology, 38(2), 70-82.
https://bit.ly/3QxTE8v
Hussain, S., Kim, Y., Thakur, S. y Breslin, J. (2022). Optimization of Waiting Time for
Electric Vehicles Using a Fuzzy Inference System. IEEE Transactions on Intelligent
Transportation Systems. 1-12. https://doi.org/10.1109/TITS.2022.3140461
Karoui, A., Alvarez, L., Goffre, T., Dherbey-Chapuis, N., Rodi, M. y Ramalho, M. (2021),
Pathways within the European Platform for Personalized Language Learning PEAPL.
Proceedings of the 29th ACM Conference on User Modeling, Adaptation and
Personalization. 90–94. https://doi.org/10.1145/3450614.3464480
López, A., González, A y Guerra, C. (2021). Desarrollo e implementación de un sistema de
inferencia difuso en un juego serio que ayude a fortalecer el razonamiento lógicomatemático. Ingenierías, 24(90), https://bit.ly/FIS_raz_log_mat
Marzal, M y Parra, P. (2021). La educación competencial desde Visual Literacy y Gaming
para la innovación educativa: propuesta para un diseño instruccional de curso.
Ibersid. 15(1), 75-83, https://bit.ly/edu_comp
Megahed, M., y Mohammed, A. (2020). Modeling Adaptive E-Learning Environment using
Facial Expressions and Fuzzy Logic. Expert Systems with Applications, 113460.
https://doi.org/10.1016/j.eswa.2020.113460
Ministerio de Educación de España. (2009). Anexo I objeto digital educativo (ODE)
definición, arquitectura, niveles de agregación y tipología. Madrid: Gobierno de
España. https://bit.ly/3qp09jl
Mohamed, F., Abdeslam, J., Lahcen, E.B. (2017). Personalization of learning activities
within a virtual environment for training based on fuzzy logic theory. Mathematics in
Natural Science. 3 (2018), 12–17 https://doi.org/10.22436/mns.03.01.02
Nafea, S., Siewe, F. y He, Y. (2018). ULEARN: Personalized Course Learning Objects Based
on Hybrid Recommendation Approach. International Journal of Information and
Education Technology, 8(12), 842-847. https://doi.org/
10.18178/ijiet.2018.8.12.1151
Núñez-Márquez, I., Rodríguez, L., Salazar, G., Castro, L., et al. (2015). A Framework for
Automatic Identification of Learning Styles in Learning Management Systems.
Research in Computing Science 106 (2015). pp. 59–68; rec. 2015-09-04; acc. 2015-
10-02. https://bit.ly/3L0gwfE
Pascual, S. (2021). Recursos Educativos Abiertos en las bibliotecas universitarias españolas:
el caso del TFG. Facultad de Filosofía y Letras de la Universidad de Zaragoza.
https://bit.ly/rec_edu_a
Quintanar, R. y Hernández, S. (2022). Modelos Tecnológicos De Aprendizaje Adaptativo
Aplicados a La Educación. Revista Tecnológica-Educativa Docentes 2.0 15 (1). 41-
66. https://doi.org/10.37843/rted.v15i1.308.
Xie, H., Chu, H., Hwang, G. & Wang, Ch. (2019). Trends and development in technologyenhanced adaptive/ personalized learning: A systematic review of journal
publications from 2007 to 2017. Computers and Education. 140 (2019).
https://doi.org/10.1016/j.compedu.2019.103599
Zhao, Q., Lai, Sh., Wang, J. y Wang, L. (2021). Hybrid Fuzzy Rule-Based Classification
System for Moodle LMS System, Journal of Internet Technology, 22(1), 81-90.
https://doi.org/10.3966/160792642021012201008
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Quintanar Casillas. , R. (2023). II. ASPIRED: ALGORITMO PROPOSTO PARA A INTEGRAÇÃO DE SEQUÊNCIAS DIDÁTICAS ADAPTATIVAS. Revista Diálogos Interdisciplinarios En Red - REDIIR, 10(10), 22. https://doi.org/10.34893/rediir.v10i10.427
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