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K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals

Published: 14 August 2017 Publication History

Abstract

Computing curricula are being developed for elementary school classrooms, yet research evidence is scant for learning trajectories that drive curricular decisions about what topics should be addressed at each grade level, at what depth, and in what order. This study presents learning trajectories based on an in-depth review of over 100 scholarly articles in computer science education research. We present three levels of results. First, we present the characteristics of the 600+ learning goals and their research context that affected the learning trajectory creation process. Second, we describe our first three learning trajectories (Sequence, Repetition, and Conditionals), and the relationship between the learning goals and the resulting trajectories. Finally, we discuss the ways in which assumptions about the context (mathematics) and language (e.g., Scratch) directly influenced the trajectories.

References

[1]
Charoula Angeli, Joke Voogt, Andrew Fluck, Mary Webb, Margaret Cox, Joyce Malyn-Smith, and Jason Zagami. 2016. A K-6 computational thinking curriculum framework: implications for teacher knowledge. Educational Technology & Society 19, 3 (2016), 47--58.
[2]
Michal Armoni and Judith Gal-Ezer. 2014. Early Computing Education: Why? What? When? Who? ACM Inroads 5, 4 (Dec. 2014), 54--59. 2153--2184 http://dx.doi.org/10.1145/2684721.2684734
[3]
Valerie Barr and Chris Stephenson. 2011. Bringing Computational Thinking to K-12: What is Involved and What is the Role of the Computer Science Education Community? ACM Inroads 2, 1 (Feb. 2011), 48--54. 2153--2184 http://dx.doi.org/10.1145/1929887.1929905
[4]
Michael T Battista. 2011. Conceptualizations and issues related to learning progressions, learning trajectories, and levels of sophistication. The Mathematics Enthusiast 8, 3 (2011), 507--570.
[5]
Karen Brennan and Mitchel Resnick. 2012. New frameworks for studying and assessing the development of computational thinking. In In AERA 2012.
[6]
Jerome Bruner. 1960. The Process of Education. The President and Fellows of Harvard College, Cambridge, MA, USA. x0--465-04627--4
[7]
Douglas H. Clements. 2002. Computers in Early Childhood Mathematics. Contemporary Issues in Early Childhood 3, 2 (2002), 160--181.
[8]
Douglas H. Clements and J Sarama. 1997. Logo: A Decade of Progress. Computers in Schools 14, 1/2 (1997), 9--46.
[9]
Douglas H. Clements and J Sarama. 2004. Learning Trajectories in Mathematics Education. Mathematical Thinking and Learning 6, 2 (2004), 81--89.
[10]
Jere Confrey, Alan P. Maloney, and Andrew K. Corley. 2014. Learning trajectories: a framework for connecting standards with curriculum. ZDM 46, 5 (2014), 719--733. 1863--9704 http://dx.doi.org/10.1007/s11858-014-0598--7
[11]
Hilary Dwyer, Charlotte Hill, Stacey Carpenter, Danielle Harlow, and Diana Franklin. 2014. 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). ACM, New York, NY, USA, 511--516. x978--1--4503--2605--6 http://dx.doi.org/10.1145/2538862.2538905
[12]
G Fessakis, E Gouli, and E Mavroudi. 2013. Problem Solving by 5--6 Years Old Kindergarten Children in a Computer Programming Environment: A Case Study. Computers & Education 63 (April 2013), 87--97. 0360--1315 https://www.learntechlib.org/p/132276
[13]
Louise P Flannery and Marina Umaschi Bers. 2013. Let's dance the "robot hokey-pokey!" Children's programming approaches and achievement throughout early cognitive development. Journal of research on technology in education 46, 1 (2013), 81--101.
[14]
Louise P. Flannery, Brian Silverman, Elizabeth R. Kazakoff, Marina Umaschi Bers, Paula Bontá, and Mitchel Resnick. 2013. Designing ScratchJr: Support for Early Childhood Learning Through Computer Programming. In Proceedings of the 12th International Conference on Interaction Design and Children (IDC '13). ACM, New York, NY, USA, 1--10. x978--1--4503--1918--8 http://dx.doi.org/10.1145/2485760.2485785
[15]
K-12 Computer Science Framework. 2016. K-12 Computer Science Framework. (2016). https://k12cs.org/
[16]
Diana Franklin, Gabriela Skifstad, Reiny Rolock, Isha Mehrotra, Valerie Ding, Alexandria Hansen, David Weintrop, and Danielle Harlow. 2017. Using Upper-Elementary Student Performance to Understand Conceptual Sequencing in a Blocks-based Curriculum. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17). ACM, New York, NY, USA, 231--236. x978--1--4503--4698--6 http://dx.doi.org/10.1145/3017680.3017760
[17]
Michelle Friend and Robert Cutler. 2013. 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). ACM, New York, NY, USA, 99--106. x978--1--4503--2243-0 http://dx.doi.org/10.1145/2493394.2493413
[18]
Ursula Fuller, Colin G Johnson, Tuukka Ahoniemi, Diana Cukierman, Isidoro Hernán-Losada, Jana Jackova, Essi Lahtinen, Tracy L Lewis, Donna McGee Thompson, Charles Riedesel, and others. 2007. Developing a computer science-specific learning taxonomy. In ACM SIGCSE Bulletin, Vol. 39. ACM, 152--170.
[19]
Chris Gregg, Luther Tychonievich, James Cohoon, and Kim Hazelwood. 2012. EcoSim: a language and experience teaching parallel programming in elementary school. In Proceedings of the 43rd ACM technical symposium on Computer Science Education. ACM, 51--56.
[20]
Shuchi Grover and Satabdi Basu. 2017. 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). ACM, New York, NY, USA, 267--272. x978--1--4503--4698--6 http://dx.doi.org/10.1145/3017680.3017723
[21]
Mark Guzdial. 2008. Education: Paving the Way for Computational Thinking. Commun. ACM 51, 8 (Aug. 2008), 25--27. 0001-0782 http://dx.doi.org/10.1145/1378704.1378713
[22]
D. Hammer and T. Sikorski. 2015. Implications of complexity for research on learning progressions. Science Education 99, 3 (2015), 424--431.
[23]
Celine Latulipe, N Bruce Long, and Carlos E Seminario. 2015. Structuring flipped classes with lightweight teams and gamification. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. ACM, 392--397.
[24]
Michael J Lee, Faezeh Bahmani, Irwin Kwan, Jilian LaFerte, Polina Charters, Amber Horvath, Fanny Luor, Jill Cao, Catherine Law, Michael Beswetherick, and others. 2014. Principles of a debugging-first puzzle game for computing education. In Visual Languages and Human-Centric Computing (VL/HCC), 2014 IEEE Symposium on. IEEE, 57--64.
[25]
Colleen M Lewis. 2010. How programming environment shapes perception, learning and goals: logo vs. scratch. In Proceedings of the 41st ACM technical symposium on Computer science education. ACM, 346--350.
[26]
John Maloney, Mitchel Resnick, Natalie Rusk, Brian Silverman, and Evelyn Eastmond. 2010. The Scratch Programming Language and Environment. Trans. Comput. Educ. 10, 4, Article 16 (Nov. 2010), 15 pages. 1946--6226 http://dx.doi.org/10.1145/1868358.1868363
[27]
John H. Maloney, Kylie Peppler, Yasmin Kafai, Mitchel Resnick, and Natalie Rusk. 2008. Programming by Choice: Urban Youth Learning Programming with Scratch. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (SIGCSE '08). ACM, New York, NY, USA, 367--371. x978--1--59593--799--5 http://dx.doi.org/10.1145/1352135.1352260
[28]
Jesús Moreno and Gregorio Robles. 2014. Automatic detection of bad programming habits in scratch: A preliminary study. In Frontiers in Education Conference (FIE), 2014 IEEE. IEEE, 1--4.
[29]
Seymour Papert. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc., New York, NY, USA. x0--465-04627--4
[30]
Kathryn Rich, Carla Strickland, and Diana Franklin. 2017. A Literature Review through the Lens of Computer Science Learning Goals Theorized and Explored in Research. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM, 495--500.
[31]
Emmanuel Schanzer, Kathi Fisler, Shriram Krishnamurthi, and Matthias Felleisen. 2015. 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). ACM, New York, NY, USA, 616--621. x978--1--4503--2966--8 http://dx.doi.org/10.1145/2676723.2677238
[32]
Linda Seiter and Brendan Foreman. 2013. 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). ACM, New York, NY, USA, 59--66. x978--1--4503--2243-0 http://dx.doi.org/10.1145/2493394.2493403
[33]
M. A. Simon. 1995. Reconstructing mathematics pedagogy from a constructivist perspective. Journal for Research in Mathematics Education (1995), 114--145.
[34]
Martin A Simon and Ron Tzur. 2004. 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.
[35]
Lieven Verschaffel, Brian Greer, and Erik De Corte. 2007. Whole number concepts and operations. In Second handbook of research on mathematics teaching and learning. Information Age Publishing, 557--628.
[36]
Linda Werner, Jill Denner, and Shannon Campe. 2015. Children programming games: a strategy for measuring computational learning. ACM Transactions on Computing Education (TOCE) 14, 4 (2015), 24.
[37]
Jeannette M. Wing. 2006. Computational Thinking. Commun. ACM 49, 3 (March 2006), 33--35. 0001-0782 http://dx.doi.org/10.1145/1118178.1118215

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  1. K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals

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      cover image ACM Conferences
      ICER '17: Proceedings of the 2017 ACM Conference on International Computing Education Research
      August 2017
      316 pages
      ISBN:9781450349680
      DOI:10.1145/3105726
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      Published: 14 August 2017

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      1. K-6
      2. computational thinking
      3. learning trajectories

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      August 18 - 20, 2017
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      Cited By

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      • (2024)Intent and Extent: Computer Science Concepts and Practices in Integrated ComputingACM Transactions on Computing Education10.1145/366482524:3(1-23)Online publication date: 14-May-2024
      • (2024)Variables and Variable Naming in Popular Programming Textbooks for Children and NovicesProceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 110.1145/3649165.3690112(242-248)Online publication date: 5-Dec-2024
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      • (2023)Domain-Specific Theories of Teaching Computing: Do they Inform Practice?Proceedings of the 23rd Koli Calling International Conference on Computing Education Research10.1145/3631802.3631810(1-15)Online publication date: 13-Nov-2023
      • (2023)Arguments for and Approaches to Computing Education in Undergraduate Computer Science ProgrammesProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633494(160-195)Online publication date: 22-Dec-2023
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