Video Recommendation for the Learning Process: A Systematic Literature Review
Abstract
This article is a systematic review of the literature on the recommen- dation of videos in the educational area. With the remarkable expansion of content over the web, finding relevant and appropriate content is not an easy task, users have particularities and preferences, which makes the process individualized. Recommendation systems assess how relevant something is to a user, and seek to develop a solution to make a personalized recommendation. It was possible with the main research question, to visualize how recommendation systems are efficient in the video based learning.
References
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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.
Published
2020-11-11
How to Cite
FERREIRA, João Pedro; CAMPOS, Lívia; MARINHO NETO, Antonio; PEREIRA JUNIOR, Cleon.
Video Recommendation for the Learning Process: A Systematic Literature Review. In: REGIONAL SCHOOL ON INFORMATICS OF 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.
