skip to main content
other

K--8 learning trajectories derived from research literature: sequence, repetition, conditionals

Published:30 January 2018Publication History
First page image

References

  1. Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J. and Zagami, J. A K-6 computational thinking curriculum framework: implications for teacher knowledge. Educational Technology & Society, 19, 3 (2016), 47--58.Google ScholarGoogle Scholar
  2. Armoni, M. and Gal-Ezer, J. Early Computing Education: Why? What? When? Who? ACM Inroads, 5, 4 (December 2014), 54--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Barr, V. and Stephenson, C. Bringing Computational Thinking to K-12: What is Involved and What is the Rold of the Computer Science Education Community? ACM Inroads 2, 1 (February 2011), 48--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Battista, M. T. Conceptualizations and issues related to learning progressions, earning trajectories, and levels of sophistication. The Mathematics Enthusiast 8, 3 (2011), 507--570.Google ScholarGoogle Scholar
  5. Brennan, K. and Resnick, M. 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, 2012), 1--25.Google ScholarGoogle Scholar
  6. Bruner, J. The Process of Education. (Cambridge, MA: Harvard University Press, 1960).Google ScholarGoogle Scholar
  7. Clements, D. H. Computers in Early Childhood Mathematics. Contemporary Issues in Early Childhood, 3, 2 (2002), 160--181.Google ScholarGoogle ScholarCross RefCross Ref
  8. Clements, D. H. and Sarama, J. Logo: A Decade of Progress. Computers in Schools, 14, 1/2 (1997), 9--46. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Clements, D. H. and Sarama, J. Learning Trajectories in Mathematics Education. Mathematical Thinking and Learning, 6, 2 (2004), 81--89.Google ScholarGoogle Scholar
  10. Confrey, J., Maloney, A. P., and Corley, A. K. Learning trajectories: a framework for connecting standards with curriculum. ZDM, 46, 5 (2014), 719--733.Google ScholarGoogle ScholarCross RefCross Ref
  11. Dwyer, H., Hill, C., Carpenter, S., Harlow, D., and Franklin, D. Identifying Elementary Students' Pre-instructional Ability to Develop Algorithms and Step-by-step Instructions. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (SIGCSE '14) (New York, NY: ACM, 2014), 511--516. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Fessakis, G., Gouli, E., and Mavroudi, E. Problem Solving by 5--6 Years Old Kindergarten Children in a Computer Programming Environment: A Case Study. Computer in Education, 63 (2013), 87--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Flannery, L. P., Bers, M. U. Let's Dance the "Robot Hokey-Pokey!" Children's Programming Approaches and Achievement through Early Cognitive Development. Journal of Research on Technology in Education, 46, 1 (2013), 81--101.Google ScholarGoogle ScholarCross RefCross Ref
  14. Flannery, L. P., Sliverman, B., Kazakoff, E. R., Bers, M. U., Bonta, P., and Resnick, M Designing Scratch Jr: Support for Early Childhood Learning through Computer Programming. In Proceedings of the 12th International Conference on Interaction Design and Children (IDC '13) (New York, NY: ACM, 2013), 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K-12 Computer Science Framework. https://k12cs.org/ Accessed 2017 September 10.Google ScholarGoogle Scholar
  16. Franklin, D., Skifstad, G., Rolock, R., Mehrota, I., Ding, V., Hansen, A., Werntrop, D. and Harlow, D. Using Upper-Elementary Student Performance to Understand Conceptual Sequencing in a Blocks-based Curriculum. In Proceedings of the 2017 ACME SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17) (New York, NY: ACM, 2013), 231--236. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Friend, M., and Cutler, R. Efficient Egg Drop Contests: How Middle School Girls Think about Algorithmic Efficiency. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research (ICER '13) (New York, NY: ACM, 2013), 99--106. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Fuller, U., Johnson, C. G., Ahoniemi, T., Cukierman, D, Hernan-Losada, I., Jackova, J., Lantinen, E., Lewis, T. L., Thompson, D. M., Riedesel, C., and others. Developing a Computer Science-specific Learning Taxonomy. ACM SIGCSE Bulletin, 39 (2007), 152--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Gregg, C., Tychonievich, L., Cohoon, J., and Hazelwood, K. EcoSim: A Language and Experience teaching parallel programming in elementary school. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (New York, NY: ACM, 2012), 51--56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Grover, S., and Basu, S. Measuring Student Learning in Introductory Block-Based Programming: Examining Misconceptions of Loops, Variables, and Boolean Logic. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17) (New York, NY: ACM, 2017), 267--272. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Grover, S., and Pea, R. Computational Thinking in K--12: A Review of the State of the Field. Educational Researcher, 42, 1 (2013, 38--43.Google ScholarGoogle ScholarCross RefCross Ref
  22. Guzdial, M. Education: Paving the Way for Computational Thinking. Communications of the ACM, 51, 8 (2008), 25--27. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Hammer, D., and Sikorski, T. Implications of Complexity for Research on Learning Progressions. Science Education, 99, 3 (2015), 424--431.Google ScholarGoogle Scholar
  24. Latulipe, C., Long, N. B., and Seminario, C. E. Structuring Flipped Classes with Lightweight Teams and Gamification. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (New York, NY: ACM, 2015), 392--397. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lee, M. J., Bahmani, F., Kwan, I., LaFerte, J., Charters, P., Horvath, A., Luor, F., Cao, J., Law, C., Beswetherick, M., and others. Principles of a Debugging-first Puzzle Game for Computing Education. In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (IEEE, 2014), 57--64.Google ScholarGoogle ScholarCross RefCross Ref
  26. Lewis, C. How Programming Environment Shapes Perception, Learning and Goals: Logo vs. Scratch. In Proceedings of the 41st Annual Technical Symposium on Computer Science Education (New York, NY: ACM, 2010), 346--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Maloney, J., Resnick, M., Rusk, N., Silverman, B., and Eastmond, E. The Scratch Programming Language and Environment. Transactions on Computing Education, 10, 4, Article 16 (2010), 15 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., and Rusk, N. Programming by Choice: Urban Youth Learning Programming with Scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (SIGCSE '08) (New York, NY: ACM, 2008), 367--371. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Moreno, J., and Robles, G. Automatic Detection of Bad Programming Habits of Scratch: A Preliminary Study. In Frontiers in Education Conference (FIE) (IEEE, 2014), 1--4.Google ScholarGoogle Scholar
  30. Papert, S. Mindstorms: Children, Computers, and Powerful Ideas. (New York, NY Basic Books, Inc., 1980). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Rich, K., Strickland, C., and Franklin, D. A Literature Review through the Lens of Computer Science Learning Goal Theorized and Explored in Research. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (New York, NY: ACM, 2017), 495--500. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Schanzer, E., Fisler, K., Krishnamurthi, S., and Felliesen, M. Transferring Skills at Solving Word Problems from Computing to Algebra Through Bootstrap. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15) (New York, NY: ACM, 2015), 616--621. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Seiter, L., and Foreman, B. Modeling the Learning Progressions of Computational Thinking of Primary Grade Students. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research (ICER '13) (New York, NY: ACM, 2013), 59--66. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Simon, M. A. Reconstructing Mathematics Pedagogy from a Constructivist Perspective. Journal for Research in Mathematics Education (1995), 114--145.Google ScholarGoogle ScholarCross RefCross Ref
  35. Simon, M. A., and Tzur, R. Explicating the Role of Mathematical Tasks in Conceptual Learning: An Elaboration of the Hypothetical Learning Trajectory. Mathematical Thinking and Learning, 6, 2 (2004), 91--104.Google ScholarGoogle Scholar
  36. Verschaffel, L., Greer, B., and De Corte, E. Whole Number Concepts and Operations. In Second Handbook of Research on Mathematics Teaching and Learning (Charlotte, NC: Information Age Publishing, 2007), 557--628.Google ScholarGoogle Scholar
  37. Werner, L., Denner, J., and Campe, S. Children Programming Games: A Strategy for Measuring Computational Learning. ACM Transactions on Computing Education, 14, 4 (2015), 24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Wing, J. M. Computational Thinking. Communications of the ACM, 49, 3 (2006), 33--35. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. K--8 learning trajectories derived from research literature: sequence, repetition, conditionals
        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

        Full Access

        • Published in

          cover image ACM Inroads
          ACM Inroads  Volume 9, Issue 1
          March 2018
          62 pages
          ISSN:2153-2184
          EISSN:2153-2192
          DOI:10.1145/3184058
          Issue’s Table of Contents

          Copyright © 2018 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 ACM 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: 30 January 2018

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • other
          • Popular
          • Refereed

        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