skip to main content
10.1145/3411764.3445571acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article
Public Access

Avoiding the Turing Tarpit: Learning Conversational Programming by Starting from Code’s Purpose

Authors Info & Claims
Published:07 May 2021Publication History

ABSTRACT

Conversational programmers want to learn about code primarily to communicate with technical co-workers, not to develop software. However, existing instructional materials don’t meet the needs of conversational programmers because they prioritize syntax and semantics over concepts and applications. This mismatch results in feelings of failure and low self-efficacy. To motivate conversational programmers, we propose purpose-first programming, a new approach that focuses on learning a handful of domain-specific code patterns and assembling them to create authentic and useful programs. We report on the development of a purpose-first programming prototype that teaches five patterns in the domain of web scraping. We show that learning with purpose-first programming is motivating for conversational programmers because it engenders a feeling of success and aligns with these learners’ goals. Purpose-first programming learning enabled novice conversational programmers to complete scaffolded code writing, debugging, and explaining activities after only 30 minutes of instruction.

References

  1. Anthony Anderson, Christina L. Knussen, and Michael R. Kibby. 1993. Teaching teachers to use HyperCard: a minimal manual approach. British Journal of Educational Technology 24, 2 (1993), 92–101.Google ScholarGoogle ScholarCross RefCross Ref
  2. John B. Black, John M. Carroll, and Stuart M. McGuigan. 1986. What Kind of Minimal Instruction Manual is the Most Effective. In Proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface(Toronto, Ontario, Canada) (CHI ’87). Association for Computing Machinery, New York, NY, USA, 159–162. https://doi.org/10.1145/29933.275623Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Kirsten Boehner, Janet Vertesi, Phoebe Sengers, and Paul Dourish. 2007. How HCI Interprets the Probes. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, CA, USA) (CHI ’07). ACM, New York, NY, USA, 1077–1086. https://doi.org/10.1145/1240624.1240789Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Karen Brennan and Mitchel Resnick. 2012. New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association (Vancouver, Canada), Vol. 1. AERA, Washington D.C., USA, 25.Google ScholarGoogle Scholar
  5. Tracy Camp, W. Richards Adrion, Betsy Bizot, Susan Davidson, Mary Hall, Susanne Hambrusch, Ellen Walker, and Stuart Zweben. 2017. Generation CS: The Growth of Computer Science. ACM Inroads 8, 2 (May 2017), 44–50. https://doi.org/10.1145/3084362Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. John M. Carroll, Penny L. Smith-Kerker, James R. Ford, and Sandra A. Mazur-Rimetz. 1987. The Minimal Manual. Hum.-Comput. Interact. 3, 2 (June 1987), 123–153. https://doi.org/10.1207/s15327051hci0302_2Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Sarah E. Chasins, Maria Mueller, and Rastislav Bodik. 2018. Rousillon: Scraping Distributed Hierarchical Web Data. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (Berlin, Germany) (UIST ’18). ACM, New York, NY, USA, 963–975. https://doi.org/10.1145/3242587.3242661Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Parmit K. Chilana, Celena Alcock, Shruti Dembla, Anson Ho, Ada Hurst, Brett Armstrong, and Philip J. Guo. 2015. Perceptions of Non-CS Majors in Intro Programming: The Rise of the Conversational Programmer. In 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, New York, NY, USA, 251–259. https://doi.org/10.1109/VLHCC.2015.7357224Google ScholarGoogle ScholarCross RefCross Ref
  9. Parmit K. Chilana, Rishabh Singh, and Philip J. Guo. 2016. Understanding Conversational Programmers: A Perspective from the Software Industry. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, CA, USA) (CHI ’16). ACM, New York, NY, USA, 1462–1472. https://doi.org/10.1145/2858036.2858323Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Alan Cooper. 2004. The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity(2 ed.). Sams, Indianapolis, IN, USA.Google ScholarGoogle Scholar
  11. Kathryn Cunningham, Rahul Agrawal Bejarano, Mark Guzdial, and Barbara Ericson. 2020. “I’m Not a Computer”: How Identity Informs Value and Expectancy During a Programming Activity. In ICLS 2020 Proceedings(The 14th International Conference of the Learning Sciences, Vol. 2). International Society of the Learning Sciences (ISLS), 705–708.Google ScholarGoogle Scholar
  12. Kathryn Cunningham, Shannon Ke, Mark Guzdial, and Barbara Ericson. 2019. Novice Rationales for Sketching and Tracing, and How They Try to Avoid It. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (Aberdeen, Scotland Uk) (ITiCSE ’19). ACM, New York, NY, USA, 37–43. https://doi.org/10.1145/3304221.3319788Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Andrew Dillon. 2012. What It Means to Be an iSchool. Journal of Education for Library and Information Science 53, 4(2012), 267–273.Google ScholarGoogle Scholar
  14. Robert Dyer, Hoan Anh Nguyen, Hridesh Rajan, and Tien N. Nguyen. 2013. Boa: A Language and Infrastructure for Analyzing Ultra-Large-Scale Software Repositories. In Proceedings of the 2013 International Conference on Software Engineering (San Francisco, CA, USA) (ICSE ’13). IEEE Press, New York, NY, USA, 422–431.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jacquelynne Eccles. 1983. Expectancies, Values and Academic Behaviors. In Achievement and Achievement Motives: Psychological and Sociological Approaches. Freeman, San Francisco, CA, USA, 75–146.Google ScholarGoogle Scholar
  16. Jacquelynne S. Eccles. 2005. Subjective Task Value and the Eccles et al. Model of Achievement-Related Choices. In Handbook of competence and motivation. Guilford Publications, New York, NY, USA, 105–121.Google ScholarGoogle Scholar
  17. Jacqueline S. Eccles. 2009. Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist 44, 2 (2009), 78.Google ScholarGoogle ScholarCross RefCross Ref
  18. Barbara J. Ericson, Mark J. Guzdial, and Briana B. Morrison. 2015. Analysis of Interactive Features Designed to Enhance Learning in an Ebook. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (Omaha, Nebraska, USA) (ICER ’15). ACM, New York, NY, USA, 169–178. https://doi.org/10.1145/2787622.2787731Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Barbara J. Ericson, Kantwon Rogers, Miranda Parker, Briana Morrison, and Mark Guzdial. 2016. Identifying Design Principles for CS Teacher Ebooks Through Design-Based Research. In Proceedings of the 2016 ACM Conference on International Computing Education Research(Melbourne, VIC, Australia) (ICER ’16). ACM, New York, NY, USA, 191–200. https://doi.org/10.1145/2960310.2960335Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kathi Fisler. 2014. The Recurring Rainfall Problem. In Proceedings of the Tenth Annual Conference on International Computing Education Research (Glasgow, Scotland, United Kingdom) (ICER ’14). Association for Computing Machinery, New York, NY, USA, 35–42. https://doi.org/10.1145/2632320.2632346Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Kathi Fisler, Shriram Krishnamurthi, and Janet Siegmund. 2016. Modernizing Plan-Composition Studies. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (Memphis, Tennessee, USA) (SIGCSE ’16). ACM, New York, NY, USA, 211–216. https://doi.org/10.1145/2839509.2844556Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Fernand Gobet, Peter CR Lane, Steve Croker, Peter CH Cheng, Gary Jones, Iain Oliver, and Julian M Pine. 2001. Chunking Mechanisms in Human Learning. Trends in cognitive sciences 5, 6 (2001), 236–243.Google ScholarGoogle Scholar
  23. Ira Greenberg. 2007. Processing: Creative Coding and Computational Art. Apress, Berkeley, CA, USA.Google ScholarGoogle Scholar
  24. Philip J. Guo. 2013. Online Python Tutor: Embeddable Web-Based Program Visualization for Cs Education. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education (Denver, Colorado, USA) (SIGCSE ’13). ACM, New York, NY, USA, 579–584. https://doi.org/10.1145/2445196.2445368Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Mark Guzdial. 1995. Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments 4, 1 (1995), 1–44.Google ScholarGoogle ScholarCross RefCross Ref
  26. Mark Guzdial, Michael Konneman, Christopher Walton, Luke Hohmann, and Elliot Soloway. 1998. Layering scaffolding and CAD on an integrated workbench: An effective design approach for project-based learning support. Interactive Learning Environments 6, 1/2 (1998), 143–179.Google ScholarGoogle ScholarCross RefCross Ref
  27. Mark Guzdial and Allison Elliott Tew. 2006. Imagineering Inauthentic Legitimate Peripheral Participation: An Instructional Design Approach for Motivating Computing Education. In Proceedings of the Second International Workshop on Computing Education Research (Canterbury, United Kingdom) (ICER ’06). ACM, New York, NY, USA, 51–58. https://doi.org/10.1145/1151588.1151597Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Kyle James Harms, Jason Chen, and Caitlin L. Kelleher. 2016. Distractors in Parsons Problems Decrease Learning Efficiency for Young Novice Programmers. In Proceedings of the 2016 ACM Conference on International Computing Education Research (Melbourne, VIC, Australia) (ICER ’16). ACM, New York, NY, USA, 241–250. https://doi.org/10.1145/2960310.2960314Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Brian Harvey and Jens Mönig. 2010. Bringing “No Ceiling” to Scratch: Can One Language Serve Kids and Computer Scientists. In Proceedings of the 2010 Constructionism Conference (Paris, France). 1–10.Google ScholarGoogle Scholar
  30. Juha Helminen, Petri Ihantola, Ville Karavirta, and Lauri Malmi. 2012. How Do Students Solve Parsons Programming Problems? An Analysis of Interaction Traces. In Proceedings of the Ninth Annual International Conference on International Computing Education Research (Auckland, New Zealand) (ICER ’12). ACM, New York, NY, USA, 119–126. https://doi.org/10.1145/2361276.2361300Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Cindy E. Hmelo and Mark Guzdial. 1996. Of Black and Glass Boxes: Scaffolding for Doing and Learning. In Proceedings of the 1996 International Conference on Learning Sciences (Evanston, Illinois) (ICLS ’96). International Society of the Learning Sciences (ISLS), 128–134.Google ScholarGoogle Scholar
  32. Luke Hohmann, Mark Guzdial, and Elliot Soloway. 1992. SODA: A Computer Aided Design Environment for the Doing and Learning of Software Design. In Proceedings of the 4th International Conference on Computer Assisted Learning(ICCAL ’92). Springer-Verlag, Berlin, Heidelberg, 307–319.Google ScholarGoogle ScholarCross RefCross Ref
  33. David Landy and Robert L. Goldstone. 2007. How Abstract Is Symbolic Thought?Journal of Experimental Psychology: Learning, Memory, and Cognition 33, 4(2007), 720.Google ScholarGoogle ScholarCross RefCross Ref
  34. Jean Lave and Etienne Wenger. 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge, UK.Google ScholarGoogle ScholarCross RefCross Ref
  35. Raymond Lister. 2011. Concrete and Other Neo-Piagetian Forms of Reasoning in the Novice Programmer. In Proceedings of the Thirteenth Australasian Computing Education Conference - Volume 114 (Perth, Australia) (ACE ’11). Australian Computer Society, Inc., AUS, 9–18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Raymond Lister, Elizabeth S. Adams, Sue Fitzgerald, William Fone, John Hamer, Morten Lindholm, Robert McCartney, Jan Erik Moström, Kate Sanders, Otto Seppälä, Beth Simon, and Lynda Thomas. 2004. A Multi-National Study of Reading and Tracing Skills in Novice Programmers. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (Leeds, United Kingdom) (ITiCSE-WGR ’04). ACM, New York, NY, USA, 119–150. https://doi.org/10.1145/1044550.1041673Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. John Maloney, Mitchel Resnick, Natalie Rusk, Brian Silverman, and Evelyn Eastmond. 2010. The Scratch Programming Language and Environment. ACM Trans. Comput. Educ. 10, 4, Article 16 (Nov. 2010), 15 pages. https://doi.org/10.1145/1868358.1868363Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. John H. Maloney, Kylie Peppler, Yasmin Kafai, Mitchel Resnick, and Natalie Rusk. 2008. Programming by choice: urban youth learning programming with scratch. In SIGCSE ’08: Proceedings of the 39th SIGCSE technical symposium on Computer science education (Portland, OR, USA). ACM, New York, NY, USA, 367–371. https://doi.org/10.1145/1352135.1352260Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Lauren Margulieux and Richard Catrambone. 2017. Using Learners’ Self-Explanations of Subgoals to Guide Initial Problem Solving in App Inventor. In Proceedings of the 2017 ACM Conference on International Computing Education Research (Tacoma, Washington, USA) (ICER ’17). Association for Computing Machinery, New York, NY, USA, 21–29. https://doi.org/10.1145/3105726.3106168Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Lauren E. Margulieux, Richard Catrambone, and Mark Guzdial. 2013. Subgoal labeled worked examples improve K-12 teacher performance in computer programming training. In Cooperative Minds: Social Interaction and Group Dynamics Proceedings of the 35th Annual Conference of the Cognitive Science Society (Berlin, Germany), M. Knauff, M. Pauen, N. Sebanz, and I. Wachsmuth (Eds.). Cognitive Science Society, Austin, TX, USA, 978–983.Google ScholarGoogle Scholar
  41. Lauren E. Margulieux, Mark Guzdial, and Richard Catrambone. 2012. Subgoal-labeled Instructional Material Improves Performance and Transfer in Learning to Develop Mobile Applications. In Proceedings of the Ninth Annual International Conference on International Computing Education Research (Auckland, New Zealand) (ICER ’12). ACM, New York, NY, USA, 71–78. https://doi.org/10.1145/2361276.2361291Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Marvin Minsky. 1974. A Framework for Representing Knowledge. Technical Report. Massachusetts Institute of Technology-AI Laboratory.Google ScholarGoogle Scholar
  43. Briana B. Morrison, Lauren E. Margulieux, Barbara Ericson, and Mark Guzdial. 2016. Subgoals Help Students Solve Parsons Problems. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (Memphis, Tennessee, USA) (SIGCSE ’16). ACM, New York, NY, USA, 42–47. https://doi.org/10.1145/2839509.2844617Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Briana B. Morrison, Lauren E. Margulieux, and Mark Guzdial. 2015. Subgoals, Context, and Worked Examples in Learning Computing Problem Solving. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research (Omaha, Nebraska, USA) (ICER ’15). ACM, New York, NY, USA, 21–29. https://doi.org/10.1145/2787622.2787733Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Fred G. Paas. 1992. Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach.Journal of Educational Psychology 84, 4 (1992), 429.Google ScholarGoogle ScholarCross RefCross Ref
  46. Dale Parsons and Patricia Haden. 2006. Parsons Programming Puzzles: A Fun and Effective Learning Tool for First Programming Courses. In Proceedings of the 8th Australasian Conference on Computing Education - Volume 52 (Hobart, Australia) (ACE ’06). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 157–163. http://dl.acm.org/citation.cfm?id=1151869.1151890Google ScholarGoogle Scholar
  47. Alan J. Perlis. 1982. Special Feature: Epigrams on Programming. ACM SIGPLAN Notices 17, 9 (1982), 7–13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Leonard Richardson. 2020. Beautiful Soup Documentation. Beautiful Soup 4.9.0. Accessed: 2020-09-15.Google ScholarGoogle Scholar
  49. Robert S. Rist. 1989. Schema Creation in Programming. Cognitive Science 13, 3 (1989), 389–414.Google ScholarGoogle ScholarCross RefCross Ref
  50. Kelly Rivers, Erik Harpstead, and Ken Koedinger. 2016. Learning Curve Analysis for Programming: Which Concepts Do Students Struggle With?. In Proceedings of the 2016 ACM Conference on International Computing Education Research (Melbourne, VIC, Australia) (ICER ’16). ACM, New York, NY, USA, 143–151. https://doi.org/10.1145/2960310.2960333Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Roger C. Schank and Robert P. Abelson. 1977. Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Lawrence Erlbaum, Hillsdale, New Jersey, USA.Google ScholarGoogle Scholar
  52. Otto Seppälä, Petri Ihantola, Essi Isohanni, Juha Sorva, and Arto Vihavainen. 2015. Do We Know How Difficult the Rainfall Problem Is?. In Proceedings of the 15th Koli Calling Conference on Computing Education Research (Koli, Finland) (Koli Calling ’15). Association for Computing Machinery, New York, NY, USA, 87–96. https://doi.org/10.1145/2828959.2828963Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Elliot Soloway. 1985. From problems to programs via plans: The content and structure of knowledge for introductory LISP programming. Journal of Educational Computing Research 1, 2 (1985), 157–172.Google ScholarGoogle ScholarCross RefCross Ref
  54. Elliot Soloway. 1986. Learning to Program = Learning to Construct Mechanisms and Explanations. Commun. ACM 29, 9 (1986), 850–858.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Elliot Soloway, Jeffrey Bonar, and Kate Ehrlich. 1983. Cognitive strategies and looping constructs: An empirical study. Commun. ACM 26, 11 (1983), 853–860.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Elliot Soloway and Kate Ehrlich. 1984. Empirical Studies of Programming Knowledge. IEEE Transactions on Software Engineering SE-10, 5 (Sept. 1984), 595–609.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Elliot Soloway, Kate Ehrlich, Jeffrey Bonar, and J. Greenspan. 1982. What do novices know about programming?In Directions in Human-Computer Interaction, Andre Badre and Ben Schneiderman (Eds.). Ablex Publishing, Norwood, New Jersey, USA, 87–122.Google ScholarGoogle Scholar
  58. Elliot M. Soloway and Beverly Woolf. 1980. Problems, Plans, and Programs. In Proceedings of the Eleventh SIGCSE Technical Symposium on Computer Science Education (Kansas City, Missouri, USA) (SIGCSE ’80). ACM, New York, NY, USA, 16–24. https://doi.org/10.1145/800140.804605Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Juha Sorva. 2013. Notional Machines and Introductory Programming Education. Transactions on Computing Education 13, 2, Article 8 (July 2013), 31 pages. https://doi.org/10.1145/2483710.2483713Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Juha Sorva, Ville Karavirta, and Lauri Malmi. 2013. A Review of Generic Program Visualization Systems for Introductory Programming Education. ACM Transactions on Computing Education 13, 4 (2013), 15.1– 15.64. https://doi.org/10.1145/2490822Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. James Clinton Spohrer. 1989. MARCEL: A Generate-Test-and-Debug (GTD) Impasse/Repair Model of Student Programmers. Ablex Publishing, Norwood, New Jersey, USA.Google ScholarGoogle Scholar
  62. James C. Spohrer and Elliot Soloway. 1985. Putting it All Together is Hard for Novice Programmers. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. Vol. March. IEEE, New York, New York, USA.Google ScholarGoogle Scholar
  63. James C. Spohrer and Elliot Soloway. 1986. Analyzing the high frequency bugs in novice programs. In Empirical Studies of Programmers Workshop, Elliot Soloway and S. Iyengar (Eds.). Ablex Publishing, Norwood, New Jersey, USA, 230–251.Google ScholarGoogle Scholar
  64. James C. Spohrer, Elliot Soloway, and Edgar Pope. 1985. A Goal/Plan Analysis of Buggy Pascal Programs. Human–Computer Interaction 1, 2 (June 1985), 163–207. https://doi.org/10.1207/s15327051hci0102_4Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive Science 12(1988), 257–285.Google ScholarGoogle ScholarCross RefCross Ref
  66. Anne Venables, Grace Tan, and Raymond Lister. 2009. A Closer Look at Tracing, Explaining and Code Writing Skills in the Novice Programmer. In Proceedings of the Fifth International Workshop on Computing Education Research Workshop (Berkeley, CA, USA) (ICER ’09). ACM, New York, NY, USA, 117–128. https://doi.org/10.1145/1584322.1584336Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Lev S. Vygotsky. 1978. Socio-cultural theory. Harvard University Press, Cambridge, MA.Google ScholarGoogle Scholar
  68. April Y. Wang, Ryan Mitts, Philip J. Guo, and Parmit K. Chilana. 2018. Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmers. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). ACM, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3174085Google ScholarGoogle ScholarDigital LibraryDigital Library
  69. David Weintrop and Uri Wilensky. 2015. To Block or Not to Block, That is the Question: Students’ Perceptions of Blocks-Based Programming. In Proceedings of the 14th International Conference on Interaction Design and Children (Boston, Massachusetts) (IDC ’15). ACM, New York, NY, USA, 199–208. https://doi.org/10.1145/2771839.2771860Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. David Wood, Jerome S Bruner, and Gail Ross. 1976. The Role of Tutoring in Problem Solving. Journal of child psychology and psychiatry 17, 2 (1976), 89–100.Google ScholarGoogle ScholarCross RefCross Ref
  71. Benjamin Xie, Dastyni Loksa, Greg L. Nelson, Matthew J. Davidson, Dongsheng Dong, Harrison Kwik, Alex Hui Tan, Leanne Hwa, Min Li, and Amy J Ko. 2019. A Theory of Instruction for Introductory Programming Skills. Computer Science Education 29, 2-3 (2019), 205–253.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Avoiding the Turing Tarpit: Learning Conversational Programming by Starting from Code’s Purpose
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
            May 2021
            10862 pages
            ISBN:9781450380966
            DOI:10.1145/3411764

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 7 May 2021

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited

            Acceptance Rates

            Overall Acceptance Rate6,199of26,314submissions,24%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format