Kathryn M. Rich
Carla Strickland
T. Andrew Binkowski
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.
- 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.Google Scholar
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Karen Brennan and Mitchel Resnick. 2012. New frameworks for studying and assessing the development of computational thinking. In In AERA 2012.Google Scholar
- Jerome Bruner. 1960. The Process of Education. The President and Fellows of Harvard College, Cambridge, MA, USA. x0--465-04627--4Google Scholar
- Douglas H. Clements. 2002. Computers in Early Childhood Mathematics. Contemporary Issues in Early Childhood 3, 2 (2002), 160--181. Google ScholarCross Ref
- Douglas H. Clements and J Sarama. 1997. Logo: A Decade of Progress. Computers in Schools 14, 1/2 (1997), 9--46. Google ScholarDigital Library
- Douglas H. Clements and J Sarama. 2004. Learning Trajectories in Mathematics Education. Mathematical Thinking and Learning 6, 2 (2004), 81--89. Google ScholarCross Ref
- 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--7Google ScholarCross Ref
- 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 Google ScholarDigital Library
- 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/132276Google ScholarDigital Library
- 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. Google ScholarCross Ref
- 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 Google ScholarDigital Library
- K-12 Computer Science Framework. 2016. K-12 Computer Science Framework. (2016). https://k12cs.org/Google Scholar
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- D. Hammer and T. Sikorski. 2015. Implications of complexity for research on learning progressions. Science Education 99, 3 (2015), 424--431. Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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. Google ScholarCross Ref
- Seymour Papert. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc., New York, NY, USA. x0--465-04627--4Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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 Google ScholarDigital Library
- 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 Google ScholarDigital Library
- M. A. Simon. 1995. Reconstructing mathematics pedagogy from a constructivist perspective. Journal for Research in Mathematics Education (1995), 114--145. Google ScholarCross Ref
- 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. Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Jeannette M. Wing. 2006. Computational Thinking. Commun. ACM 49, 3 (March 2006), 33--35. 0001-0782 http://dx.doi.org/10.1145/1118178.1118215 Google ScholarDigital Library
Index Terms
- K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals
Recommendations
A K-8 Debugging Learning Trajectory Derived from Research Literature
SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science EducationCurriculum development is dependent on the following question: What are the learning goals for a specific topic, and what are reasonable ways to organize and order those goals? Learning trajectories (LTs) for computational thinking (CT) topics will help ...
Learning Trajectories in Action: A Practical Study on an After-School Coding Club Curriculum
SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science EducationComputing curricula are finding their way into many elementary and middle school students' classrooms and after-school learning experiences. As more curricula are developed, there is a need to understand how they align with the Rich et al. learning ...
K12 outreach: motivating k12 school counselors to embrace the changing face of computing (abstract only)
SIGCSE '13: Proceeding of the 44th ACM technical symposium on Computer science educationK12 students have limited access to computer science curricula in United States' public schools. Individuals' perceptions of the field as a masculine, elite, and individualistic profession can restrict which K12 students are encouraged to pursue careers ...
Comments