An Approach for Personalized Recommendation of Educational Materials through Content-Based Filtering in Virtual Learning Environments
DOI:
https://doi.org/10.5753/rbie.2023.3292Keywords:
Adaptive Systems, Recommendation, Personalized LearningAbstract
The Adaptive and Intelligent Educational Systems area is constantly evolving and aims to create personalized learning environments through the application of recent technologies, including Artificial Intelligence techniques, combined with pedagogical theories. This work aims to contribute to the area of AI in education, using an approach that combines Semantic Web technologies and a bio-inspired algorithm to perform personalized recommendation of learning objects through content-based filtering.In contrast to other approaches, this study combines repositories of Virtual Learning Environments (VLE) with materials available on the Web (YouTube and Wikipedia) to provide educational resources in diverse formats on a specific topic. Web materials are retrieved and structured as learning objects. The approach was tested in the Classroom eXperience (CX) VLE, and an extension resource was also created for Moodle. Experiments were carried out to test the approach. One of the experiments aimed to analyze students' opinions regarding personalized recommendation. Students positively evaluated recommendations that considered their knowledge level and offered additional materials on the topic. Another experiment considered three different recommendation processes to observe students' preferences. Recommendations considered the use and non-use of learning styles in the process. The overall average rating was relatively better when ignoring the use of learning styles, but there was no statistical significance.
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Almahairi, A., Kastner, K., Cho, K., & Courville, A. (2015). Learning distributed representations from reviews for collaborative filtering. Proceedings of the 9th ACM Conference on Recommender Systems, 147–154. https://doi.org/10.1145/2792838.2800192. [GS Search]
An, D., & Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116, 410–416. https://doi.org/10.1016/j.paid.2017.04.050. [GS Search].
Araújo, R. D. (2017). Uma Arquitetura Computacional para Autoria e Personalização de Objetos de Aprendizagem em Ambientes Educacionais Ubíquos (tese de dout.). Universidade Federal de Uberlândia. [GS Search].
Ariyaratne, M., & Fernando, T. (2014). A comparative study on nature inspired algorithms with firefly algorithm. International Journal of Engineering and Technology, 4(10), 611–617. [GS Search].
Belizário Júnior, C. (2018). Reúso de conteúdo da Web na recomendação personalizada de objetos de aprendizagem: uma abordagem baseada em um algoritmo genético, tecnologias da Web Semântica e uma ontologia (diss. de mestr.). Universidade Federal de Uberlândia. [GS Search].
Belizário Júnior, C., & Dorça, F. (2018). Uma abordagem para a criação e recomendaçao de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), 29, 1533. [GS Search].
Bernhard, K., & Vygen, J. (2008). Combinatorial optimization: Theory and algorithms. Springer, Third Edition, 2005.
Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13, 159–172. [GS Search].
Colchester, K., Hagras, H., Alghazzawi, D., & Aldabbagh, G. (2017). A survey of artificial intelligence techniques employed for adaptive educational systems within e-learning platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1), 47–64. https://doi.org/10.1515/jaiscr-2017-0004. [GS Search].
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT press. [GS Search].
De Medio, C., Limongelli, C., Marani, A., & Taibi, D. (2019). Retrieval of Educational Resources from the Web: A Comparison Between Google and Online Educational Repositories. Em M. A. Herzog, Z. Kubincová, P. Han & M. Temperini (Ed.), Advances in Web-Based Learning – ICWL 2019 (pp. 28–38). Springer International Publishing. [GS Search].
Dias, L. L., Barbosa, J. S., Barrére, E., & de Souza, J. F. (2017). Uma abordagem para identificação de similaridade entre recursos educacionais utilizando bases de conhecimento externas. Revista Brasileira de Informática na Educação, 25(02), 18. https://doi.org/10.5753/rbie.2017.25.02.18. [GS Search].
Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. Em Recommender systems handbook (pp. 421–451). Springer. [GS Search].
Dwivedi, P., Kant, V., & Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2), 819–836. https://doi.org/10.1007/s10639-017-9637-7. [GS Search].
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674–681. [GS Search].
Feldman, J., Monteserin, A., & Amandi, A. (2015). Automatic detection of learning styles: state of the art. Artificial Intelligence Review, 44(2), 157–186. https://doi.org/10.1007/s10462-014-9422-6. [GS Search].
Ferreira, H. N. M. (2018). Uma Abordagem Híbrida Baseada em Redes Bayesianas e Ontologias para Modelagem do Estudante em Sistemas Adaptativos e Inteligentes para Educação (tese de dout.). Universidade Federal de Uberlândia. https://doi.org/10.5753/cbie.sbie.2017.1197. [GS Search].
Ferreira, J. P. B., de Tôrres Maschio, P., de Santana, T. S., da Costa, N. T., & Pereira Junior, C. (2020). Análise de Vídeos como Recurso Educacional em Plataforma Não Formal de Aprendizagem. Anais do XXXI Simpósio Brasileiro de Informática na Educação, 1733–1742. https://doi.org/10.5753/cbie.sbie.2017.1197 https://doi.org/10.5753/cbie.sbie.2017.1197. [GS Search].
Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. NY: Basics. [GS Search].
Gasparetti, F., De Medio, C., Limongelli, C., Sciarrone, F., & Temperini, M. (2018). Prerequisites between learning objects: Automatic extraction based on a machine learning approach. Telematics and Informatics, 35(3), 595–610. https://doi.org/10.1016/j.tele.2017.05.007. [GS Search].
Group, N. E. T. P. T. W., et al. (2010). Transforming American education: Learning powered by technology.
Harman, K., & Koohang, A. (2007). Learning objects: standards, metadata, repositories, and LCMS. Informing Science. [GS Search].
Kalogeraki, E.-M., Troussas, C., Apostolou, D., Virvou, M.,&Panayiotopoulos, T. (2016). Ontology-based model for learning object metadata. 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), 1–6. https://doi.org/10.1109/IISA.2016.7785383. [GS Search].
Kelly, D., & Tangney, B. (2006). Adapting to intelligence profile in an adaptive educational system. Interacting with Computers, 18(3), 385–409. https://doi.org/10.1016/j.intcom.2005.11.009. [GS Search].
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006. [GS Search].
Kolb, A. Y., & Kolb, D. A. (2005). Learning styles and learning spaces: Enhancing experiential learning in higher education. Academy of management learning & education, 4(2), 193– 212. https://doi.org/10.1016/j.chb.2013.06.036. [GS Search].
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. Em Recommender systems handbook (pp. 73–105). Springer. https://doi.org/10.1007/978-0-387-85820-3_3. [GS Search].
LTSC, I. (2020). 1484.12.1-2020 - IEEE Standard for Learning Object Metadata.
Pereira Junior, C. X., Dorça, F. A., & Araujo, R. D. (2019). Towards an Adaptive Approach that Combines Semantic Web Technologies and Metaheuristics to Create and Recommend Learning Objects. 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2161, 395–397. https://doi.org/10.1109/ICALT.2019.00118. [GS Search].
Pereira Júnior, C., Araújo, R. D., & Dorça, F. A. (2020). Recomendação Personalizada de Conteúdo Instrucional Complementar usando Repositório de Objetos de Aprendizagem e Recursos da Web. Anais do XXXI Simpósio Brasileiro de Informática na Educação, 1293– 1302. ttps://doi.org/10.5753/cbie.sbie.2020.1293. [GS Search].
Pereira Júnior, C., Belizario Júnior, C., Araújo, R., & Dorça, F. (2020). Personalized Recommendation of Learning Objects Through Bio-inspired Algorithms and Semantic Web Technologies: an Experimental Analysis. Anais do XXXI Simpósio Brasileiro de Informática na Educação, 1333–1342. https://doi.org/10.5753/cbie.sbie.2020.1333. [GS Search].
Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. Proceedings of the fifth ACM conference on Recommender systems, 157–164. https://doi.org/10.1145/2043932.2043962. [GS Search].
Roy, D., Sarkar, S., & Ghose, S. (2008). Automatic Extraction of Pedagogic Metadata from Learning Content. Int. J. Artif. Intell. Ed., 18(2), 97–118. [GS Search].
Soloman, B. A., & Felder, R. M. (2005). Index of learning styles questionnaire. NC State University. Available online at: [Link] (last visited on 14.05. 2010), 70. [GS Search].
Valaski, J., Malucelli, A., & Reinehr, S. (2011). Revisão dos modelos de estilos de aprendizagem aplicados à adaptação e personalização dos materiais de aprendizagem. Anais do Simpósio Brasileiro de Informática na Educação, 1(1). [GS Search].
Vargha, A., & Delaney, H. D. (1998). The Kruskal-Wallis test and stochastic homogeneity. Journal of Educational and behavioral Statistics, 23(2), 170–192. https://doi.org/10.3102/10769986023002170. [GS Search].
Wan, S., & Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource. Knowledge-Based Systems, 160, 71–87. https://doi.org/10.1016/j.knosys.2018.06.014. [GS Search].
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Copyright (c) 2023 Cleon Xavier Pereira Júnior, Rafael Dias Araújo, Fabiano Azevedo Dorça
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