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Youth Computational Participation in the Wild: Understanding Experience and Equity in Participating and Programming in the Online Scratch Community

Published:28 August 2017Publication History
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Abstract

Most research in primary and secondary computing education has focused on understanding learners within formal classroom communities, leaving aside the growing number of promising informal online programming communities where young users contribute, comment, and collaborate on programs to facilitate learning. In this article, we examined trends in computational participation in Scratch, an online community with over 1 million registered youth designers. Drawing on a random sample of 5,004 youth programmers and their activities over 3 months in early 2012, we examined programming concepts used in projects in relation to level of participation, gender, and length of membership of Scratch programmers. Latent class analysis results identified the same four groups of programmers in each month based on the usage of different programming concepts and showed how membership in these groups shifted in different ways across time. Strikingly, the largest group of project creators (named Loops) used the simplest and fewest programming concepts. Further, this group was the most stable in membership and was disproportionately female. In contrast, the more complex programming groups (named Variables, Low Booleans, and High Booleans) showed much movement across time. Further, the Low Booleans and High Booleans groups, the only groups to use “and,” “or,” and “not” statements in their programs, were disproportionately male. In the discussion, we address the challenges of analyzing young learners’ programming in informal online communities and opportunities for designing more equitable computational participation.

References

  1. C. Aragon, S. Poon, A. Monroy-Hernández, and D. Aragon. 2009. A tale of two online communities: Fostering collaboration and creativity in scientists and children. In Proceedings of the Creativity and Cognition Conference. New York: ACM Press, 9--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Berland, R. S. Baker, and P. Blikstein. 2014. Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning 19, 1--2, 205--220.Google ScholarGoogle ScholarCross RefCross Ref
  3. M. Berland, T. Martin, T. Benton, C. P. Smith, and D. Davis. 2013. Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences 22, 4, 564--99. Google ScholarGoogle ScholarCross RefCross Ref
  4. P. Blikstein, M. Worsley, C. Piech, M. Sahami, S. Cooper, and D. Koller. 2014. Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences 23, 4, 561--599. Google ScholarGoogle ScholarCross RefCross Ref
  5. K. Brennan. 2013. Best of Both Worlds: Issues of Structure and Agency in Computational Creation, in and out of School. Unpublished Dissertation. Cambridge, MA: Massachusetts Institute of Technology.Google ScholarGoogle Scholar
  6. K. Brennan and M. Resnick. 2012. New Frameworks for Studying and Assessing the Development of Computational Thinking. Paper presented at annual American Educational Research Association meeting. Vancouver, BC, Canada.Google ScholarGoogle Scholar
  7. A. S. Bruckman. 1997. MOOSE Crossing: Construction, Community, and Learning in a Networked Virtual World for Kids. Unpublished Dissertation. Cambridge, MA: Massachusetts Institute of Technology.Google ScholarGoogle Scholar
  8. S. Dasgupta, W. Hale, A. Monroy-Hernández, and B. M. Hill. 2016. Remixing as a pathway to computational thinking. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work 8 Social Computing (CSCW’16). New York: ACM Press, 1438--1449. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Denner, L. Werner, S. Campe, and E. Ortiz. 2014. Pair programming: Under what conditions is it advantageous for middle school students? Journal of Research on Technology in Education 46, 3, 277--296 Google ScholarGoogle ScholarCross RefCross Ref
  10. D. A. Fields, M. T. Giang, and Y. B. Kafai. 2014. Programming in the wild: Patterns of computational participation in the Scratch online social networking forum. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (WiPSCE’14). ACM, New York, 2--11. http://doi.acm.org/10.1145/2670757.2670768 Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. A. Fields and Y. B. Kafai. 2009. A connective ethnography of peer knowledge sharing and diffusion in a tween virtual world. International Journal of Computer Supported Collaborative Learning 4, 1, 47--68. Google ScholarGoogle ScholarCross RefCross Ref
  12. D. A. Fields and Y. B. Kafai. 2010. Knowing and throwing mudballs, hearts, piece, and flowers: A connective ethnography of gaming practices. Games and Culture 5, 1, 43--63. Google ScholarGoogle ScholarCross RefCross Ref
  13. D. A. Fields, Y. B. Kafai, and M. T. Giang. 2016. Participation by choice: A transitional analysis of patterns in social networking and coding contributions in the online Scratch community. In Mass Collaboration and Education. U. Cress, H. Jeong, and J. Moskaliuk (Eds.). New York: Springer, 209--240. Google ScholarGoogle ScholarCross RefCross Ref
  14. D. A. Fields, K. Pantic, and Y. B. Kafai. 2015. “I have a tutorial for this”: The language of online peer support in the Scratch programming community. In Proceedings of Interaction Design and Children (IDC’15). ACM, New York, 229--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. A. Fields, L. Quirke, J. Amely, and J. Maughan. 2016. Combining big data and thick data analyses for understanding youth learning trajectories in a Summer Coding Camp. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education. ACM, 150--155. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. A. Fields, V. Vasudevan, and Y. B. Kafai. 2015. The programmers’ collective: Fostering participatory culture by making music videos in a high school Scratch coding workshop. Interactive Learning Environments 23, 5, 1--21. Google ScholarGoogle ScholarCross RefCross Ref
  17. N. F. Velasquez, D. A. Fields, D. Olsen, H. Taylor Martin, A. Strommer, M. C. Sheperd, and Y. B. Kafai. 2013. Novice programmers talking about projects: What automated text analysis reveals about online Scratch users’ comments. In Proceedings of the 2014 47th Hawaii International Conference on System Sciences (HICSS’14). IEEE Computer Society, Washington, DC, 1635--1644.Google ScholarGoogle Scholar
  18. J. P. Gee. 2004. Situated Language and Learning: A Critique of Traditional Schooling. New York and London: Routledge.Google ScholarGoogle Scholar
  19. S. M. Grimes and D. A. Fields. 2012. Kids Online: A New Research Agenda for Understanding Social Networking Forums. The Joan Ganz Cooney Center at Sesame Workshop, New York. Available online at http://www.joanganzcooneycenter.org/reports-38.html.Google ScholarGoogle Scholar
  20. S. M. Grimes and D. A. Fields. 2015. Children's media making, but not sharing: The potential and limitations of child-specific DIY media websites for a more inclusive media landscape. Media International Australia 154, 112--122. Google ScholarGoogle ScholarCross RefCross Ref
  21. S. Grover and R. Pea. 2013. Computational thinking in K--12 a review of the state of the field. Educational Researcher 42, 1, 38--43. Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Guzdial. 2015. Learner-Centered Design of Computing Education: Research on Computing for Everyone. San Rafeal, CA: Morgan 8 Claypool.Google ScholarGoogle Scholar
  23. J. Hagenaars and A. McCutcheon (Eds). 2002. Applied Latent Class Analysis. Cambridge, UK: Cambridge University Press. Google ScholarGoogle ScholarCross RefCross Ref
  24. B. M. Hill and A. Monroy-Hernández. 2017. A longitudinal dataset of five years of public activity in the Scratch online community. Scientific Data 4:170002. Google ScholarGoogle ScholarCross RefCross Ref
  25. M. Ito, S. Baumer, M. Bittanti, D. Boyd, R. Cody, B. Herr, H. A. Horst, P. G. Lange, D. Mahendran, K. Martinez, C. J. Pascoe, D. Perkel, L. Robinson, C. Sims, and L. Tripp. 2010. Hanging Out, Messing Around, and Geeking Out: Living and Learning with New Media. Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  26. Y. B. Kafai. 1995 Minds in Play: Computer Game Design as a Context for Children's Learning. New York: Routledge.Google ScholarGoogle Scholar
  27. Y. B. Kafai and W. Quinn Burke. 2014. Connected Code: Why Children Need to Learn Programming. Cambridge, MA: MIT Press.Google ScholarGoogle ScholarCross RefCross Ref
  28. Y. B. Kafai and D. A. Fields. 2013. Connected Play: Tweens in a Virtual World. Cambridge, MA: MIT Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y. B. Kafai, D. A. Fields, R. Roque, W. Quinn Burke, and A. Monroy-Hernández. 2012. Collaborative agency in youth online and offline creative production in Scratch. Research and Practice in Technology Enhanced Learning 7, 2, 63--87.Google ScholarGoogle Scholar
  30. Y. F. Kafai, K. A. Peppler, and R. N. Chapman. 2009. The Computer Clubhouse: Constructionism and Creativity in Youth Communities. New York: Teachers College Press.Google ScholarGoogle Scholar
  31. C. Kelleher and R. Pausch. 2007. Using storytelling to motivate programming. Communications of the ACM 50, 7, 58--64. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. J. H. Maloney, K. Peppler, Y. B. Kafai, M. Resnick, and N. Rusk. 2008. Programming by choice: Urban youth learning programming with scratch. ACM SIGCSE Bulletin 40, 1, 367--371. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. J. Margolis, R. Estrella, J. Goode, J. Holme, and K. Nao. 2008. Stuck in the Shallow End: Education, Race, and Computing. Cambridge, MA: MIT Press.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. J. Margolis and A. Fisher. 2002. Unlocking the Clubhouse. Cambridge, MA: MIT Press.Google ScholarGoogle Scholar
  35. J. N. Matias, S. Dasgupta, and B. M. Hill. 2016. Skill progression in scratch revisited. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI’16). ACM, New York, 1486--1490. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. A. Monroy-Hernández. 2012. Designing for Remixing: Supporting an Online Community of Amateur Creators. Unpublished Dissertation. Cambridge, MA: MIT.Google ScholarGoogle Scholar
  37. B. Muthen. 2002. Statistical and substantive checking in growth mixture modeling. Retrieved January 2007 from http://www.gseis.ucla.edu/faculty/muthen/full_paper_list.h.Google ScholarGoogle Scholar
  38. B. Muthen and L. Muthen. 2001. Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcohol Clinical Experimental Research 24, 6, 882--891. Google ScholarGoogle ScholarCross RefCross Ref
  39. K. Pantic, D. A. Fields, and D. A. Lisa Quirke. 2016. Studying situated learning in a constructionist programming camp: A multimethod microgenetic analysis of one girl's learning pathway. In Proceedings of the 15th International Conference on Interaction Design and Children. ACM, 428--439. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. L. Porter, M. Guzdial, C. McDowell, and B. Simon. 2013. Success in introductory programming: What works? Communications of ACM 56, 8, 34--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. A. Repenning, D. C. Webb, K. H. Koh, H. Nickerson, S. B. Miller, C. Brand, I. Her Many Horses, A. Basawapatna, F. Gluck, R. Grover, K. Gutierrez, and N. Repenning. 2015. Scalable game design: A strategy to bring systemic computer science education to schools through game design and simulation creation. ACM Transactions on Computer Education 15, 2, 265--269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. M. Resnick, J. Maloney, A. Monroy-Hernández, N. Rusk, E. Eastmond, K. Brennan, A. D. Millner, E. Rosenbaum, J. Silver, B. Silverman, and Y. B. Kafai. 2009. Scratch: Programming for everyone. Communications of the ACM 52, 11, 60--67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. G. T. Richard and Y. B. Kafai. 2016. Blind spots in youth DIY programming: Examining diversity in creators, content and comments within the Scratch online community. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1473--1485. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. C. Scaffidi and C. Chambers. 2012. Skill progression demonstrated by users in the Scratch animation environment. International Journal of Human-Computer Interaction 28, 6, 383--398. Google ScholarGoogle ScholarCross RefCross Ref
  45. E. Soloway and J. Spohrer. 1990. Empirical Studies of Novice Programmers. Norwood, NJ: Ablex Publishing.Google ScholarGoogle Scholar
  46. L. Werner, C. McDowell, and J. Denner. 2013. A first step in learning analytics: Pre-processing low-level Alice logging data of middle school students. Journal of Educational Data Mining 5, 2, 11--37.Google ScholarGoogle Scholar
  47. White House. January 30, 2016. FACT SHEET: President Obama announces computer science for all initiative. Washington, DC: Office of the Press Secretary. Available at: https://www.whitehouse.gov/the-press-office/2016/01/30/fact-sheet-president-obama-announces-computer-science-all-initiative-0Google ScholarGoogle Scholar
  48. S. Yang, C. Domeniconi, M. Revelle, M. Sweeney, B. U. Gelman, C. Beckley, and A. Johri. 2015. Uncovering trajectories of informal learning in large online communities of creators. In Proceedings of the 2nd (2015) ACM Conference on Learning @ Scale (L@S’15). ACM, New York, 131--140.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Transactions on Computing Education
        ACM Transactions on Computing Education  Volume 17, Issue 3
        Special Issue on Learning Analytics
        September 2017
        116 pages
        EISSN:1946-6226
        DOI:10.1145/3135995
        Issue’s Table of Contents

        Copyright © 2017 ACM

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        Publication History

        • Published: 28 August 2017
        • Accepted: 1 May 2017
        • Revised: 1 March 2017
        • Received: 1 October 2016
        Published in toce Volume 17, Issue 3

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