A K-8 Debugging Learning Trajectory Derived from Research Literature

Curriculum 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 to guide emerging curriculum development efforts for computer science in elementary school. This study describes the development of an LT for Debugging. We conducted a rigorous analysis of scholarly research on K-8 computer science education to extract what concepts in debugging students should and are capable of learning. The concepts were organized into the LT presented within. In this paper, we describe the three dimensions of debugging that emerged during the creation of the trajectory: (1) strategies for finding and fixing errors, (2) types of errors, and (3) the role of errors in problem solving. In doing so, we go beyond identification of specific debugging strategies to further articulate knowledge that would help students understand when to use those techniques and why they are successful. Finally, we illustrate how the Debugging LT has guided our efforts to develop an integrated mathematics and CT curriculum for grades 3-5.

[1]  John Maloney,et al.  The Scratch Programming Language and Environment , 2010, TOCE.

[2]  Taylor Martin,et al.  Using Learning Analytics to Understand the Learning Pathways of Novice Programmers , 2013 .

[3]  Margaret M. Burnett,et al.  Principles of a debugging-first puzzle game for computing education , 2014, 2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[4]  Filiz Kalelioglu,et al.  A new way of teaching programming skills to K-12 students: Code.org , 2015, Comput. Hum. Behav..

[5]  A. Diamond,et al.  Interventions Shown to Aid Executive Function Development in Children 4 to 12 Years Old , 2011, Science.

[6]  Diana Franklin,et al.  A Literature Review through the Lens of Computer Science Learning Goals Theorized and Explored in Research , 2017, SIGCSE.

[7]  Martin A. Simon Reconstructing Mathematics Pedagogy from a Constructivist Perspective. , 1995 .

[8]  Diana Franklin,et al.  Decomposition: A K-8 Computational Thinking Learning Trajectory , 2018, ICER.

[9]  Diana Franklin,et al.  K--8 learning trajectories derived from research literature , 2018, Inroads.

[10]  M. Battista Conceptualizations and Issues related to Learning Progressions, Learning Trajectories, and Levels of Sophistication , 2011, The Mathematics Enthusiast.

[11]  D. Clements Computers in Early Childhood Mathematics , 2002 .

[12]  Michael S. Horn,et al.  Defining Computational Thinking for Mathematics and Science Classrooms , 2016 .

[13]  Shuchi Grover,et al.  Computational Thinking in K–12 , 2013 .

[14]  Shuchi Grover,et al.  Measuring Student Learning in Introductory Block-Based Programming: Examining Misconceptions of Loops, Variables, and Boolean Logic , 2017, SIGCSE.

[15]  Marina Umaschi Bers,et al.  Let’s Dance the “Robot Hokey-Pokey!” , 2013 .

[16]  Evangelia Gouli,et al.  Problem solving by 5-6 years old kindergarten children in a computer programming environment: A case study , 2013, Comput. Educ..

[17]  Linda M. Seiter,et al.  Modeling the learning progressions of computational thinking of primary grade students , 2013, ICER.

[18]  Judith Gal-Ezer,et al.  Early computing education , 2014, Inroads.

[19]  John B. Black,et al.  Supporting Debugging Skills: Using Embodied Instructions in Children’s Programming Education , 2017 .

[20]  James P. Cohoon,et al.  EcoSim: a language and experience teaching parallel programming in elementary school , 2012, SIGCSE '12.

[21]  Jeannette M. Wing An introduction to computer science for non-majors using principles of computation , 2007, SIGCSE.

[22]  Sharon M. Carver,et al.  Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer , 1988, Cognitive Psychology.

[23]  David Hammer,et al.  Implications of Complexity for Research on Learning Progressions , 2015 .

[24]  Ursula Fuller,et al.  Developing a computer science-specific learning taxonomy , 2007, ACM SIGCSE Bull..

[25]  J. Sarama,et al.  Learning Trajectories in Mathematics Education , 2004 .

[26]  Diana Franklin,et al.  Using Upper-Elementary Student Performance to Understand Conceptual Sequencing in a Blocks-based Curriculum , 2017, SIGCSE.

[27]  Chris Stephenson,et al.  Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? , 2011, INROADS.

[28]  Diana Franklin,et al.  Computational Thinking for Physics: Programming Models of Physics Phenomenon in Elementary School , 2014 .

[29]  Mary Webb,et al.  A K-6 Computational Thinking Curriculum Framework: Implications for Teacher Knowledge , 2016, J. Educ. Technol. Soc..

[30]  B. Reiser,et al.  Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners , 2009 .

[31]  Diana Franklin,et al.  Identifying elementary students' pre-instructional ability to develop algorithms and step-by-step instructions , 2014, SIGCSE.

[32]  Linda M. Seiter Using SOLO to Classify the Programming Responses of Primary Grade Students , 2015, SIGCSE.

[33]  Jere Confrey,et al.  Learning trajectories: a framework for connecting standards with curriculum , 2014 .