Characterizing Computational Thinking in High School Science

This study identifies high school students’ computational thinking practices in the context of science, technology, engineering, and math (CT-STEM practices) and the relationships between their practices-in-use. More specifically, we explore the CT-STEM practices that emerged as a result of students’ participation in a two-day biology lesson featuring the exploration of a computational model on predator-prey dynamics. Digitally recorded data were taken from seventy-six students across four classes of one teacher. By applying a grounded analysis to students’ written responses to two different assessment items embedded within the lesson, we found four CT-STEM practices related to identifying a model’s limitations and eight practices related to exploring the model. Applying a network analysis to responses coded for these practices, we found networks representing common patterns of practices-in-use. This work identifies the informal CT-STEM practices that students bring to their learning and models combinations of practices-in-use.

[1]  David Williamson Shaffer,et al.  Local Versus Global Connection Making in Discourse , 2016, ICLS.

[2]  Aditi Wagh,et al.  Bridging inquiry‐based science and constructionism: Exploring the alignment between students tinkering with code of computational models and goals of inquiry , 2017 .

[3]  A. diSessa Toward an Epistemology of Physics , 1993 .

[4]  Richard J. Murnane,et al.  The New Division of Labor: How Computers Are Creating the Next Job Market , 2004 .

[5]  Curt Burgess,et al.  Producing high-dimensional semantic spaces from lexical co-occurrence , 1996 .

[6]  George Siemens,et al.  Learning Analytics and Educational Data Mining , 2016 .

[7]  Andrea A. diSessa,et al.  Meta-representation: an introduction , 2000 .

[8]  Zachari Swiecki,et al.  Teaching and Assessing Engineering Design Thinking with Virtual Internships and Epistemic Network Analysis , 2015 .

[9]  Jonathan M. Borwein,et al.  Exploratory Experimentation and Computation , 2011 .

[10]  David A. Gillam,et al.  A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas , 2012 .

[11]  Andrew Elby,et al.  On the Form of a Personal Epistemology , 2002 .

[12]  Ian Foster,et al.  2020 Computing: A two-way street to science's future , 2006, Nature.

[13]  R. Estrella,et al.  Stuck in the Shallow End Education , Race , and Computing , 2008 .

[14]  David Williamson Shaffer,et al.  Epistemic Network Analysis: A Worked Example of Theory-Based Learning Analytics , 2017 .

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

[16]  J. Roschelle,et al.  Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition , 1994 .

[17]  Michael S. Horn,et al.  Fostering computational literacy in science classrooms , 2014, CACM.

[18]  Elizabeth Bagley,et al.  Epistemic network analysis : a Prototype for 21 st Century assessment of Learning , 2009 .

[19]  Allan Collins,et al.  Design Research: Theoretical and Methodological Issues , 2004 .