Recomendação de Vídeos para o Processo de Aprendizagem: uma Revisão Sistemática da Literatura
Resumo
Este artigo trata-se de uma Revisão Sistemática da Literatura acerca da recomendação de vídeos no âmbito educacional. Com a notável expansão de conteúdos pela web, encontrar um conteúdo relevante e apropriado não é uma tarefa fácil, usuários possuem particularidades e preferências, o que torna o processo individualizado. Os sistemas de recomendação avaliam como algo é relevante para um usuário, e buscam desenvolver uma solução para efetuar uma recomendação personalizada. Foi possível com a questão de pesquisa principal, visualizar como sistemas de recomendação mostram-se eficientes na aprendizagem baseada em vídeo.
Referências
Basu, S., Yu, Y., Singh, V., and Zimmermann, R. (2016). Videopedia: Lecture video recommendation for educational blogs using topic modeling.
Bezerra, S. F., Silva, S., Neto, F. M., and Silva, P. (2017). Youtubeontology: Uma ontologia do youtube para auxiliar um sistema de recomendação ubíqua de conteúdos. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 28, page 1787.
Bhabad, D. T., Therese, S., and Gedam, M. (2017). Multimedia based information retrieval approach based on asr and ocr and video recommendation system. In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pages 1168–1172. IEEE.
Bhatt, C., Cooper, M., and Zhao, J. (2018). Seqsense: Video recommendation using topic sequence mining. In International Conference on Multimedia Modeling, pages 252–263. Springer.
Dias, L., Barrere, E., and Souza, J. (2020). The impact of semantic annotation techniques on content-based video lecture recommendation. Journal of Information Science, page 016555152093173.
Jun, X. and Min, W. (2015). A novel design of education video personalized recommendation system based on collaborative filtering recommendation technology. In Ubiquitous Computing Application and Wireless Sensor, pages 471–480. Springer.
Jung, S., Son, M., il Kim, C., Rew, J., and Hwang, E. (2019). Video-based learning assistant scheme for sustainable education. New Review of Hypermedia and Multimedia, 25(3):161–181.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Medrek, J., Otto, C., and Ewerth, R. (2018). Recommending scientific videos based on metadata enrichment using linked open data. In International Conference on Theory and Practice of Digital Libraries, pages 286–292. Springer.
Mulholland, E., Mc Kevitt, P., Lunney, T., and Schneider, K.-M. (2016). Analysing emotional sentiment in people’s youtube channel comments. In Interactivity, Game Creation, Design, Learning, and Innovation, pages 181–188. Springer.
Pereira, C. K., Campos, F., Ströele, V., David, J. M. N., and Braga, R. (2018). Broad-rsi–educational recommender system using social networks interactions and linked data. Journal of Internet Services and Applications, 9(1):7.
Shu, J., Shen, X., Liu, H., Yi, B., and Zhang, Z. (2018). A content-based recommendation algorithm for learning resources. Multimedia Systems, 24(2):163–173.
Vieira, M. A. et al. (2016). Modelagem de espaços inteligentes pessoais e espaços inteligentes fixos no contexto de cenários de computação ubíqua.
Xu, W. and Zhou, Y. (2020). Course video recommendation with multimodal information in online learning platforms: A deep learning framework. British Journal of Educational Technology, n/a(n/a).
Yu, C. (2020). Research on the innovation and integrated development of college ideological and political work based on short video recommendation model. Journal of Physics: Conference Series, 1533:042038.
Zhang, J., Zhang, Y., Wu, X., and Li, G. (2018). Teaching video recommendation based on student evaluation. In International Conference on Cloud Computing and Security, pages 182–190. Springer.
Zhou, P., Zhou, Y., Wu, D., and Jin, H. (2016). Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks. IEEE transactions on multimedia, 18(6):1217–1229.
Zhu, H., Liu, Y., Tian, F., Ni, Y., Wu, K., Chen, Y., and Zheng, Q. (2018). A cross-curriculum video recommendation algorithm based on a video-associated knowledge map. IEEE Access, 6:57562–57571.
Bezerra, S. F., Silva, S., Neto, F. M., and Silva, P. (2017). Youtubeontology: Uma ontologia do youtube para auxiliar um sistema de recomendação ubíqua de conteúdos. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 28, page 1787.
Bhabad, D. T., Therese, S., and Gedam, M. (2017). Multimedia based information retrieval approach based on asr and ocr and video recommendation system. In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), pages 1168–1172. IEEE.
Bhatt, C., Cooper, M., and Zhao, J. (2018). Seqsense: Video recommendation using topic sequence mining. In International Conference on Multimedia Modeling, pages 252–263. Springer.
Dias, L., Barrere, E., and Souza, J. (2020). The impact of semantic annotation techniques on content-based video lecture recommendation. Journal of Information Science, page 016555152093173.
Jun, X. and Min, W. (2015). A novel design of education video personalized recommendation system based on collaborative filtering recommendation technology. In Ubiquitous Computing Application and Wireless Sensor, pages 471–480. Springer.
Jung, S., Son, M., il Kim, C., Rew, J., and Hwang, E. (2019). Video-based learning assistant scheme for sustainable education. New Review of Hypermedia and Multimedia, 25(3):161–181.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Medrek, J., Otto, C., and Ewerth, R. (2018). Recommending scientific videos based on metadata enrichment using linked open data. In International Conference on Theory and Practice of Digital Libraries, pages 286–292. Springer.
Mulholland, E., Mc Kevitt, P., Lunney, T., and Schneider, K.-M. (2016). Analysing emotional sentiment in people’s youtube channel comments. In Interactivity, Game Creation, Design, Learning, and Innovation, pages 181–188. Springer.
Pereira, C. K., Campos, F., Ströele, V., David, J. M. N., and Braga, R. (2018). Broad-rsi–educational recommender system using social networks interactions and linked data. Journal of Internet Services and Applications, 9(1):7.
Shu, J., Shen, X., Liu, H., Yi, B., and Zhang, Z. (2018). A content-based recommendation algorithm for learning resources. Multimedia Systems, 24(2):163–173.
Vieira, M. A. et al. (2016). Modelagem de espaços inteligentes pessoais e espaços inteligentes fixos no contexto de cenários de computação ubíqua.
Xu, W. and Zhou, Y. (2020). Course video recommendation with multimodal information in online learning platforms: A deep learning framework. British Journal of Educational Technology, n/a(n/a).
Yu, C. (2020). Research on the innovation and integrated development of college ideological and political work based on short video recommendation model. Journal of Physics: Conference Series, 1533:042038.
Zhang, J., Zhang, Y., Wu, X., and Li, G. (2018). Teaching video recommendation based on student evaluation. In International Conference on Cloud Computing and Security, pages 182–190. Springer.
Zhou, P., Zhou, Y., Wu, D., and Jin, H. (2016). Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks. IEEE transactions on multimedia, 18(6):1217–1229.
Zhu, H., Liu, Y., Tian, F., Ni, Y., Wu, K., Chen, Y., and Zheng, Q. (2018). A cross-curriculum video recommendation algorithm based on a video-associated knowledge map. IEEE Access, 6:57562–57571.
Publicado
11/11/2020
Como Citar
FERREIRA, João Pedro; CAMPOS, Lívia; MARINHO NETO, Antonio; PEREIRA JUNIOR, Cleon.
Recomendação de Vídeos para o Processo de Aprendizagem: uma Revisão Sistemática da Literatura. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 8. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 191-200.
DOI: https://doi.org/10.5753/erigo.2020.13873.
