Predictive Video Analytics in Online Courses: A Systematic Literature Review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Education,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10758-023-09697-z.pdf
Reference142 articles.
1. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550. https://doi.org/10.1016/j.chb.2013.05.031
2. Akazaki, J. M., Machado, L. R., & Behar, P. A. (2022). Learning analytics to identify the socio-affective scenarios in a virtual learning environment. In V. L. Uskov, R. J. Howlett, & L. C. Jain (Eds.), Smart education and E-learning-smart pedagogy (pp. 199–208). NewYork: Springer.
3. Akçapınar, G., & Bayazıt, A. (2018). Investigating video viewing behaviors of students with different learning approaches. Turkish Online Journal of Distance Education, 19(4), 116–125. https://doi.org/10.17718/tojde.471907
4. Alhammad, M. M., & Moreno, A. M. (2018). Gamification in software engineering education: A systematic mapping. Journal of Systems and Software, 141, 131–150. https://doi.org/10.1016/j.jss.2018.03.065
5. Ali, O., Ally, M., & Dwivedi, Y. (2020). The state of play of blockchain technology in the financial services sector: A systematic literature review. International Journal of Information Management, 54, 102199. https://doi.org/10.1016/j.ijinfomgt.2020.102199
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