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Scaling Expert Feedback: Two Case Studies

Published:12 April 2017Publication History

ABSTRACT

Traditionally, education relies on a linear relationship between enrollment and staff; rising enrollment dictates increases to staff with some expertise (such as teaching assistants, TAs) for evaluation. This relationship is expensive, so learning at scale has largely deemphasized expert evaluation and feedback. Two organizations, though, have used different models to scale up class size online while retaining this expert evaluation and feedback. In this paper, we analyze the methods these two organizations have used to increase enrollment while preserving scalability and feedback. We observe an academic program has scaled feedback with traditional TAs by relying on unique characteristics of its student body, while a commercial program has done so with a novel, network-based model. These successes show the potential of learning from experts at scale.

References

  1. Aleven, V., Mclaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105--154.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Boud, D., Cohen, R., & Sampson, J. (2001). Peer Learning in Higher Education: Learning from & with Each Other. Psychology Press.Google ScholarGoogle Scholar
  3. Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of educational research, 65(3), 245--281. Google ScholarGoogle ScholarCross RefCross Ref
  4. Carey, K. (2016, October 5). Georgia Tech's $7,000 Online Master's Degree Could Start a Revolution. New York Times, pp. A15.Google ScholarGoogle Scholar
  5. Falchikov, N., & Goldfinch, J. (2000). Student peer assessment in higher education: A meta-analysis comparing peer and teacher marks. Review of Educational Research, 70(3), 287--322. Google ScholarGoogle ScholarCross RefCross Ref
  6. Geigle, C., Zhai, C., & Ferguson, D. C. (2016, April). An Exploration of Automated Grading of Complex Assignments. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp. 351--360). ACM.Google ScholarGoogle Scholar
  7. Goodman, J., Melkers, J., & Pallais, A. (2016). Can Online Delivery Increase Access to Education? HKS Faculty Research Working Paper Series RWP16-035.Google ScholarGoogle Scholar
  8. Goel, A. & Joyner, D. A. (2016a). An Experiment in Teaching Cognitive Systems Online. In Haynes, D. (Ed.) International Journal for Scholarship of Technology-Enhanced Learning 1(1).Google ScholarGoogle Scholar
  9. Goel, A. & Joyner, D. (2016b). Formative Assessment and Implicit Feedback in Online Learning. Presentation, Learning with MOOCs III.Google ScholarGoogle Scholar
  10. Hext, J. B., & Winings, J. W. (1969). An automatic grading scheme for simple programming exercises. Communications of the ACM, 12(5), 272--275. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jensen, J. C., Lee, E. A., & Seshia, S. A. (2013, April). Virtualizing cyber-physical systems: Bringing CPS to online education. In Proc. First Workshop on CPS Education (CPS-Ed).Google ScholarGoogle Scholar
  12. Joyner, D. A., Ashby, W., Irish, L., Lam, Y., Langston, J., Lupiani, I., ... & Bruckman, A. (2016, April). Graders as Meta-Reviewers: Simultaneously Scaling and Improving Expert Evaluation for Large Online Classrooms. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp. 399--408). ACM.Google ScholarGoogle Scholar
  13. Joyner, D. A., Goel, A., & Isbell, C. (2016). The Unexpected Pedagogical Benefits of Making Higher Education Accessible. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kendall, K., & Schussler, E. (2012). Does instructor type matter? Undergraduate student perception of graduate teaching assistants and professors. CBE-Life Sciences Education, 11(2), 187--199. Google ScholarGoogle ScholarCross RefCross Ref
  15. Kirschner, P., Sweller, J., & Clark, R. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75--86. Google ScholarGoogle ScholarCross RefCross Ref
  16. Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Toward datadriven design with the OLEI scale. ACM Transactions on Computer-Human Interaction (TOCHI), 22(2), 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kolhe, P., Littman, M. L., & Isbell, C. L. (2016, April). Peer Reviewing Short Answers using Comparative Judgement. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp. 241--244). ACM.Google ScholarGoogle Scholar
  18. Kulkarni, C., Bernstein, M. S., & Klemmer, S. (2015). PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance. In Proceedings from The Second ACM Conference on Learning @ Scale. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Lu, Y., Warren, J., Jermaine, C., Chaudhuri, S., & Rixner, S. (2015, May). Grading the Graders: Motivating Peer Graders in a MOOC. In Proceedings of the 24th International Conference on World Wide Web (pp. 680--690). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lundstrom, K., & Baker, W. (2009). To give is better than to receive: The benefits of peer review to the reviewer's own writing. Journal of Second Language Writing, 18(1), 30--43. Google ScholarGoogle ScholarCross RefCross Ref
  21. Murray, T., Blessing, S., & Ainsworth, S. (2003). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Springer Science & Business Media. Google ScholarGoogle ScholarCross RefCross Ref
  22. Polson, M. C., & Richardson, J. J. (2013). Foundations of intelligent tutoring systems. Psychology Press.Google ScholarGoogle Scholar
  23. Raman, K., & Joachims, T. (2015). Bayesian Ordinal Peer Grading. In Proceedings from The Second ACM Conference on Learning @ Scale. ACM. 149--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529--556. Google ScholarGoogle ScholarCross RefCross Ref
  25. Salzmann, C., Gillet, D., & Piguet, Y. (2016, February). MOOLs for MOOCs: A first edX scalable implementation. In 2016 13th International Conference on Remote Engineering and Virtual Instrumentation (REV) (pp. 246--251). IEEE. Google ScholarGoogle ScholarCross RefCross Ref
  26. Saraf, K. & Smith, C. (2016, October 5). "Coursera's superheroes: Meet the Mentor team." Coursera Blog. Retrieved from coursera.tumblr.com/post/151389966612/Google ScholarGoogle Scholar
  27. Srikant, S., & Aggarwal, V. (2014, August). A system to grade computer programming skills using machine learning. In Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining (pp. 1887--1896). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227--265.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197--221. Google ScholarGoogle ScholarCross RefCross Ref
  30. Van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155--174. Google ScholarGoogle ScholarCross RefCross Ref
  31. Van Zundert, M., Sluijsmans, D., & Van Merriënboer, J. (2010). Effective peer assessment processes: Research findings and future directions. Learning and Instruction, 20(4), 270--279. Google ScholarGoogle Scholar
  32. Vogelsang, T., & Ruppertz, L. (2015, March). On the validity of peer grading and a cloud teaching assistant system. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 41--50). ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Yaron, D., Karabinos, M., Lange, D., Greeno, J. G., & Leinhardt, G. (2010). The ChemCollective-virtual labs for introductory chemistry courses. Science, 328(5978), 584--585. Google ScholarGoogle ScholarCross RefCross Ref

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    • Published in

      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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      Publication History

      • Published: 12 April 2017

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

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