Personalized Recommendation of Complementary Instructional Content using Learning Objects Repository and Web Resources
Abstract
This article presents a proposal for personalized recommendation of Learning Objects (LOs) in the form of complementary content for a class. Considering that educational repositories are more restricted than existing contenton the Web, this work also investigates the extension of materials, using Wikipedia and Youtube to better support the student’s learning process. The proposal uses Content-Based Filtering (CBF), taking into account students’ preferences and classes metadata. The results show that the support of external content generates better adaptability and diversification in the instructional content delivered to the student.
Keywords:
Learning Object Recommendation, Content-Based Filtering, Complementary Educational Content, Educational Web Resources
References
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Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Ljubojevic, M., Vaskovic, V., Stankovic, S., and Vaskovic, J. (2014). Using supplementary video in multimedia instruction as a teaching tool to increase efficiency of learning and quality of experience. International Review of Research in Open and Distributed Learning, 15(3):275–291.
Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook. Springer.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook, pages 387–415. Springer.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., and Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach.Telematics and Informatics, 34(5).
Selwyn, N. and Gorard, S. (2016). Students’ use of wikipedia as an academic resource-patterns of use and perceptions of usefulness. The Internet and Higher Education, 28.
Wan, S. and Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource.Knowledge-Based Systems, 160:71 – 87.
An, D. and Carr, M. (2017). Learning styles theory fails to explain learning and achievement: Recommendations for alternative approaches. Personality and Individual Differences, 116:410–416.
Araújo, R. D. (2017). Uma Arquitetura Computacional para Autoria e Personalização de Objetos de Aprendizagem em Ambientes Educacionais Ubíquos. PhD thesis, Universidade Federal de Uberlândia.
Araújo, R. D., Brant-Ribeiro, T., Ferreira, H. N., Dorça, F. A., and Cattelan, R. G. (2020).Using learning styles for creating and personalizing educational content in ubiquitous learning environments. Revista Brasileira de Informática na Educação, 28:133.
Curran, V., Simmons, K., Matthews, L., Fleet, L., Gustafson, D. L., Fairbridge, N. A., and Xu, X. (2020). Youtube as an educational resource in medical education: a scopingreview. Medical Science Educator, pages 1–8.
De Medio, C., Limongelli, C., Marani, A., and Taibi, D. (2019). Retrieval of educational resources from the web: A comparison between google and online educational repositories. In International Conference on Web-Based Learning, pages 28–38. Springer.
Felder, R. M., Silverman, L. K., et al. (1988). Learning and teaching styles in engineering education.Engineering education, 78(7):674–681.
IEEE LTSC (2001). IEEE 1484.12.1 - Draft Standard for Learning Object Metadata. Learning Technology Standards Committee of the IEEE.
Júnior, C. B. and Dorça, F. (2018). Uma abordagem para a criação e recomendação de objetos de aprendizagem usando um algoritmo genético, tecnologias da web semântica e uma ontologia. In Simpósio Brasileiro de Informática na Educação.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106:166–171.
Ljubojevic, M., Vaskovic, V., Stankovic, S., and Vaskovic, J. (2014). Using supplementary video in multimedia instruction as a teaching tool to increase efficiency of learning and quality of experience. International Review of Research in Open and Distributed Learning, 15(3):275–291.
Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook. Springer.
Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., and Koper, R. (2011). Recommender systems in technology enhanced learning. In Recommender systems handbook, pages 387–415. Springer.
Pal, S., Pramanik, P. K. D., Majumdar, T., and Choudhury, P. (2019). A semi-automatic metadata extraction model and method for video-based e-learning contents. Education and Information Technologies, 24(6):3243–3268.
Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., and Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach.Telematics and Informatics, 34(5).
Selwyn, N. and Gorard, S. (2016). Students’ use of wikipedia as an academic resource-patterns of use and perceptions of usefulness. The Internet and Higher Education, 28.
Wan, S. and Niu, Z. (2018). An e-learning recommendation approach based on the self-organization of learning resource.Knowledge-Based Systems, 160:71 – 87.
Published
2020-11-24
How to Cite
PEREIRA JÚNIOR, Cleon; ARAÚJO, Rafael D.; DORÇA, Fabiano A..
Personalized Recommendation of Complementary Instructional Content using Learning Objects Repository and Web Resources. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1293-1302.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1293.
