Video-based Big Data Analytics in Cyberlearning
DOI:
https://doi.org/10.18608/jla.2017.42.5Keywords:
e-learning, video index, big data, learning analytics, assessmentAbstract
In this paper, we present a novel system, inVideo, for video data analytics, and its use in transforming linear videos into interactive learning objects. InVideo is able to analyze video content automatically without the need for initial viewing by a human. Using a highly efficient video indexing engine we developed, the system is able to analyze both language and video frames. The time-stamped commenting and tagging features make it an effective tool for increasing interactions between students and online learning systems. Our research shows that inVideo presents an efficient tool for learning technology research and increasing interactions in an online learning environment. Data from a cybersecurity program at the University of Maryland show that using inVideo as an adaptive assessment tool, interactions between student–student and student–faculty in online classrooms increased significantly across 24 sections program-wide.
References
Ángel, S., Javier, T., Pablo, M., & Baltasar, F. (2012). Tracing a little for big improvements: Application of learning analytics and videogames for student assessment. Proceedings of the 4th IEEE International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES ’12), 28 October 2012, Genoa, Italy (pp. 203–209). http://dx.doi.org/10.1016/j.procs.2012.10.072
Bakshi, K. (2012). Considerations for big data: Architecture and approach. Proceedings of the IEEE Aerospace Conference, 3–10 March 2012, Big Sky, MT, USA (pp. 1–7).
Choi, H. J., & Johnson, S. D. (2005). The effect of context-based video instruction on learning and motivation in online courses. The American Journal of Distance Education, 19(4), 215–227. http://dx.doi.org/10.1207/s15389286ajde1904_3
Dyckhoff, A., Zielke, D., Bültmann, M., Chatti, M., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Journal of Educational Technology & Society, 15(3), 58–76.
Elias, T. (2011). Learning analytics: Definitions, processes and potential. Retrieved from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf
Haubold, A., & Kender, J. R. (2004). Analysis and visualization of index words from audio transcripts of instructional videos. Proceedings of the IEEE Symposium on Multimedia Software Engineering, 1-3 March 2004, Norfolk, VA, USA (pp. 570–573).
Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20.
Rideout, V., Foehr, U., & Roberts, D. (2010). Generation M2: Media in the lives of 8- to 18-year-olds. Kaiser Family Foundation. Retrieved from http://kaiserfamilyfoundation.files.wordpress.com/2013/01/8010.pdf
Wang, S., & Behrmann, M. (2009). Automatic adaptive assessment in mLearning. Proceedings of the International Conference on Cognition and Exploratory Learning in Digital Age (IADIS CELDA 2009), 20–22 November 2009, Rome, Italy (pp. 435–438).
Wang, S., & Behrmann, M. (2010). Video indexing and automatic transcript creation. Proceedings of the 2nd International Conference on Education Research. Retrieved from http://kihd.gmu.edu/assets/docs/kihd/activ/new/Wang_Behrman_press.pdf
Wang, S., Chen, J., & Behrmann, M. (2004). Visualizing search engine results of data-driven web content. Retrieved from http://www.w3.org/WAI/RD/2003/12/Visualization/VisSearch/VisualSearchEngine.htm
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