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A Crowdsourcing Approach to Collecting Tutorial Videos -- Toward Personalized Learning-at-Scale

Published:12 April 2017Publication History

ABSTRACT

We investigated the feasibility of crowdsourcing full- fledged tutorial videos from ordinary people on the Web on how to solve math problems related to logarithms. This kind of approach (a form of learnersourcing [9, 11]) to efficiently collecting tutorial videos and other learning resources could be useful for realizing personalized learning-at-scale, whereby students receive specific learning resources -- drawn from a large and diverse set -- that are tailored to their individual and time-varying needs. Results of our study, in which we collected 399 videos from 66 unique "teachers" on Mechanical Turk, suggest that (1) approximately 100 videos -- over 80% of which are mathematically fully correct -- can be crowdsourced per week for $5/video; (2) the average learning gains (posttest minus pretest score) associated with watching the videos was stat. sig. higher than for a control video (0.105 versus 0.045); and (3) the average learning gains (0.1416) from watching the best tested crowdsourced videos was comparable to the learning gains (0.1506) from watching a popular Khan Academy video on logarithms.

References

  1. T. Aleahmad, V. Aleven, and R. Kraut. Creating a corpus of targeted learning resources with a web-based open authoring tool. IEEE Transactions on Learning Technologies, 2(1):3--9, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. R. Anderson, C. F. Boyle, and B. J. Reiser. Intelligent tutoring systems. Science, 228(4698):456--462, 1985. Google ScholarGoogle Scholar
  3. J. L. Booth, K. E. Lange, K. R. Koedinger, and K. J. Newton. Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction, 25:24--34, 2013. Google ScholarGoogle ScholarCross RefCross Ref
  4. P. Brusilovsky and C. Peylo. Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13:159--172, 2003.Google ScholarGoogle Scholar
  5. C.-M. Chen. Intelligent web-based learning system with personalized learning path guidance. Computers & Education, 51(2):787--814, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y. Chen, T. Mandel, Y.-E. Liu, and Z. Popovic. Crowdsourcing accurate and creative word problems and hints. 2016.Google ScholarGoogle Scholar
  7. N. T. Heffernan, K. S. Ostrow, K. Kelly, D. Selent, E. G. Inwegen, X. Xiong, and J. J. Williams. The future of adaptive learning: Does the crowd hold the key? International Journal of Artificial Intelligence in Education, pages 1--30, 2016. Google ScholarGoogle ScholarCross RefCross Ref
  8. G.-J. Hwang, F.-R. Kuo, P.-Y. Yin, and K.-H. Chuang. A heuristic algorithm for planning personalized learning paths for context-aware ubiquitous learning. Computers & Education, 54(2):404--415, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Kim, P. J. Guo, D. T. Seaton, P. Mitros, K. Z. Gajos, and R. C. Miller. Understanding in-video dropouts and interaction peaks inonline lecture videos. In Proceedings of Learning at Scale, pages 31--40. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. J. Kim, R. C. Miller, and K. Z. Gajos. Learnersourcing subgoal labeling to support learning from how-to videos. In CHI'13 Extended Abstracts on Human Factors in Computing Systems, pages 685--690. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Kim, P. T. Nguyen, S. Weir, P. J. Guo, R. C. Miller, and K. Z. Gajos. Crowdsourcing step-by-step information extraction to enhance existing how-to videos. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pages 4017--4026. ACM, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. O. Polozov, E. O'Rourke, A. M. Smith, L. Zettlemoyer, S. Gulwani, and Z. Popovic. Personalized mathematical word problem generation. In IJCAI, pages 381--388, 2015.Google ScholarGoogle Scholar
  13. L. P. Salamanca, A. R. Carini, M. A. Lee, K. Dykstra, J. Whitehill, D. Angus, J. Wiles, J. S. Reilly, and M. S. Bartlett. Characterizing the temporal dynamics of student-teacher discourse. In International conference on Development and Learning, pages 1--2, 2012. Google ScholarGoogle ScholarCross RefCross Ref
  14. S. Türkay. The effects of whiteboard animations on retention and subjective experiences when learning advanced physics topics. Computers & Education, 98:102--114, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. VanLehn, C. Lynch, K. Schulze, J. A. Shapiro, R. Shelby, L. Taylor, D. Treacy, A. Weinstein, and M. Wintersgill. The andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence in Education, 15(3):147--204, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. J. Williams, J. Kim, A. Rafferty, S. Maldonado, W. Lasecki, K. Gajos, and N. Heffernan. Axis: Generating explanations at scale with learnersourcing and machine learning. In ACM Learning at Scale, 2016.Google ScholarGoogle Scholar
  17. B. Woolf, W. Burleson, I. Arroyo, T. Dragon, D. Cooper, and R. Picard. Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3--4):129--164, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          L@S '17: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale
          April 2017
          352 pages
          ISBN:9781450344500
          DOI:10.1145/3051457

          Copyright © 2017 ACM

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          • Published: 12 April 2017

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          L@S '17 Paper Acceptance Rate14of105submissions,13%Overall Acceptance Rate117of440submissions,27%

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