Computational Thinking in STEM Education

James F. Newland – Department of Curriculum and Instruction, College of Education, University of Houston

Modern science practice often depends on computational tools for modeling and simulation, data analysis and visualization, creating computer programs to solve problems, and understanding a system as a series of parts. Could students learn to use this sort of computational thinking while constructing science knowledge in a modern classroom? This study will focus on introductory physics and astronomy courses.

Seymour Papert used the term computational thinking (CT) in his 1980 work Mindstorms, in which he described integrating computing into learning. In more recent work, Papert (1996) describes how computational tools allow learners to construct knowledge either through rapid calculation or by discovering or re-imagining fundamental constructs in a STEM subject. Using this definition of CT provides for students to learn science by doing science using computational tools.

Dr. Jeannette Wing first mentioned computational thinking (CT) in 2006 as a series of fundamental principles that extend out of computer science as a lot more than computer programming or coding but also abstraction, recursion, data science, and systems thinking (2006).

There are a lot of frameworks out there. One pre-publication paper by Weller et al. does a meta-analysis of many of the most cited frameworks coupled with a mixed-methods study to create a physics education framework that could lead to concept inventory style assessments (2021). But the most relevant to my work is the CT framework from Weintrop et al., which lists four major domains for CT: data practices, modeling and simulation practices, computational problem-solving practices, and systems thinking practices (2016). Using Orban and Teeling-Smith, my focus is mainly on coding as a way of targeting different elements within the CT framework (2020).

As a matter of practice, I have begun integrating CT into both the astronomy course I teach and the AP Physics C course, which covers calculus-based mechanics in the Fall semester and calculus-based electricity and magnetism. Almost every area of both courses touch one or more of the CT framework elements. Some CT curricular items from the literature involve hands-on inquiry-driven data collection, analysis, and visualization. Some CT curricular works from literature tackle modeling and simulations. Here, coding allows for hands-on modeling and simulation rather than a black-box form of modeling and simulation. The development of algorithms for solutions to scientific problems uses coding, but the expertise needed to start a coding solution from scratch means that some scaffolding is necessary. Weller et al. refer to minimally working programs to reduce the cognitive load of science students developing algorithms to solve science problems (2021). The most abstract of CT applications in science comes from systems thinking and the interconnection between systems levels.

The focus of my research is how CT can positively and negatively impact science learning. I am using the computational thinking framework with the four domains outlined by Weintrop et al. (2016). In this short paper, the literature evaluated focuses mainly on physics undergraduate coursework. My research areas are focused on 9-12 science students, undergraduate science students, and science teachers in research programs, both formal and informal.

Some topics in introductory undergraduate astronomy courses can be hard to turn into lab activities without coding. The use of free web-based coding platforms can allow access to real-world data in a scaffolded format (Newland, 2020). Recent web infrastructure advancements and increased device access will enable students to code just by using the web. As a result of the virtual learning environment forced upon my classes, I created a series of remote activities for astronomy and physics. These activities are web-based and all activities and code run in the browser. Some are posted via GitHub for all to use (Newland, 2021b). Recent work to bring all the various ways CT is being used in physics education was shared at the American Association of Physics Teachers winter meeting (Newland, 2021a).

References

Newland, J. (2021). Integrating Coding into Astronomy Remotely [conference presentation]. https://docs.google.com/presentation/d/1bSJXbcHRO66d3h4CXHCcPkua4_9ZoGOcuYhLVpf5LM0/edit?usp=sharing

Newland, J. (2021). jimmynewland/colabnotebooks: Google Colab Notebooks. https://doi.org/10.5281/ZENODO.4318058

Newland, J. (2020). Teaching with Code: Globular Cluster Distance Lab. Research Notes of the AAS, 4(7), 118. https://doi.org/10.3847/2515-5172/aba953

Orban, C. M., & Teeling-Smith, R. M. (2020). Computational Thinking in Introductory Physics. The Physics Teacher, 58(4), 247–251. https://doi.org/10.1119/1.5145470

Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc. https://dl.acm.org/doi/book/10.5555/1095592

Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95–123. https://doi.org/10.1007/BF00191473

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5

Weller, D. P., Bott, T. E., Caballero, M. D., & Irving, P. W. (2021). Developing a learning goal framework for computational thinking in computationally integrated physics classrooms [Manuscript submitted for publication]. 1–46. http://arxiv.org/abs/2105.07981

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215