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Analisa

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A video learning analytics tool for Present@ and other video platforms

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Analisa

  1. 1. Analis@ a video learning analytics tool for Present@ and other video platforms Conesa, J., Córcoles, C., Pérez-Navarro, A., Santanach, F., Garcia Ríos, M. uoc.edu
  2. 2. In the beginning… Multimedia learning. Richard E. Mayer, 2001, Cambridge Press Multimedia learning principles (coherence, signaling, redundancy, spatial and temporal contiguity, segmenting, pre-training, modality, multimedia, personalization, voice, image) Clark, Ruth C., and Richard E. Mayer. E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons, 2016
  3. 3. The worked-out example principle Renkl, Alexander. "The worked-out-example principle in multimedia learning." The Cambridge handbook of multimedia learning (2005): 229-245.
  4. 4. The self-explanation principle Roy, Marguerite, and Michelene TH Chi. "The self-explanation principle in multimedia learning." The Cambridge handbook of multimedia learning (2005): 271-286. Renkl, Alexander. "Learning mathematics from worked-out examples: Analyzing and fostering self-explanations." European Journal of Psychology of Education 14.4 (1999): 477-488.
  5. 5. So, we would like to transfer efficient self- explanation… …but, it turns out, finding the efficient self-explainers is not that easy. (Especially if you work fully online…)
  6. 6. We need some learning analytics Kim, Juho, et al. "Data-driven interaction techniques for improving navigation of educational videos." Proceedings of the 27th annual ACM symposium on User interface software and technology. ACM, 2014. Giannakos, Michail N., Konstantinos Chorianopoulos, and Nikos Chrisochoides. "Collecting and making sense of video learning analytics." 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. IEEE, 2014. Shi, Conglei, et al. "VisMOOC: Visualizing video clickstream data from massive open online courses." 2015 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2015.
  7. 7. There are other benefits, too
  8. 8. Existing solutions do not fit our needs
  9. 9. Existing solutions do not fit our needs http://popcornjs.org/ (plus some PHP and JavaScript)
  10. 10. Collected data is not easy to read…
  11. 11. We need a way to visualize it
  12. 12. The bad news The data is extremely noisy and doesn’t capture all that is happening You need it to correlate it to the content (Koumi, Jack. Designing video and multimedia for open and flexible learning. Routledge, 2006) … And, of course, learning analytics, by itself, is not going to solve the problem Not everything that can be counted counts. Not everything that counts can be counted. Albert Einstein (?)
  13. 13. End of (that) story. Meanwhile… https://youtu.be/gAaP9jqfciM
  14. 14. Analis@ https://www.uoc.edu/portal/en/elearncenter/innovacio/projectes/fitxes-projectes/projecte-12.html
  15. 15. From Popcorn to H5P (and from MySQL to a MongoDB database, better integrated with the institution’s datawarehouse) https://h5p.org/
  16. 16. Present@’s features • Are students watching videos when we expect them to? • Which students? When? For how long? • Are there segments that are viewed more? Which ones? • What’s the student behaviour? Pausing, skipping… • Does student behaviour correlate in any way to learning / academic performance? (Will we finally be able to locate efficient self explainers?) • …
  17. 17. Thank you! Questions? César Córcoles ccorcoles@uoc.edu

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