Fostering Computational Thinking Through Engineering Design Activities in a High School Biology Course
Ido Davidesco, Bianca Montrosse‐Moorhead, Dylan Boczar, Julia Oas, Mary-Kate Coburn, Aaron M. Kyle, Leslie G. Bondaryk · Proceedings. · 2025
There is a critical need to incorporate Computational Thinking (CT) into a broad range of STEM courses to promote access to computing education.This study explored whether this could be achieved through engineering design activities embedded in a high school biology course.An interdisciplinary team of researchers and teachers developed a curricular unit where high school students design and program a simplified model of a bionic arm from the sensors up.Quantitative findings revealed significant pre-to-post improvements in some aspects of CT and in engineering design self-efficacy.Qualitative data highlighted students' initial apprehension with programming and engineering tasks, which evolved into engagement through scaffolded support and the use of a visual programming interface.The real-world context of the unit motivated students by linking CT and engineering to biological concepts.This study illustrates the potential of interdisciplinary approaches to the integration of CT and engineering design in STEM courses. Objectives and significanceWhile Computational Thinking (CT) originated in computer science, CT practices like abstraction, decomposition, and algorithmic thinking are now embedded in virtually every Science, Technology, Engineering, and Mathematics (STEM) discipline (Malyn-Smith et al., 2018).Recognizing its interdisciplinary relevance, recent reforms in K-12 science education emphasize CT as a core science and engineering practice (NGSS Lead States, 2013).There is a critical need to integrate CT into non-computer science courses, such as biology, to demonstrate the interdisciplinary and fundamental nature of CT and to provide better access to all students (National Research Council, 2012).Biology is a promising context for CT development because computational tools are widely used in biology research (e.g., in vaccine development), yet CT is rarely incorporated into biology courses (Hsu, Chang, & Hung, 2021).Additionally, embedding CT in required science courses (e.g., biology) could expand the reach of CT education.Even though the term CT was coined nearly 20 years ago by Wing (2006), building on earlier work by Papert (1980), an exact definition of CT remains elusive.In the current paper, we will use the following definition: "Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent" (Wing, 2010; p. 1).Our conceptual framework builds on a highly cited CT taxonomy by Weintrop et al. (2016).This taxonomy consists of 22 distinct practices mapped to four broad categories: data practices, modeling and simulation practices, computational problem solving, and systems thinking.Our conceptual framework also highlights the close alignment between CT practices and engineering design.Although the literature presents a variety of models for the design process, most models include (a) problem identification; (b) solution design generation; (c) assessment of solutions against problem requirements; and (d) iterative redesign and refinement (National Research Council, 2012).Each step in the engineering process can potentially engage multiple CT practices (Jacques, 2020).For example, problem identification could encompass "preparing problems for computational solutions" and "defining systems and managing complexity" (both are CT practices listed in the Weintrop et al. taxonomy).Importantly, shifting the focus from coding to engineering design could reduce barriers to entry for teachers and students.The goal of the current study was to explore how CT can be incorporated into a non-computer science course through engineering design activities.The following research questions were explored: (1) how do students' CT thought processes change throughout their participation in a CT-intensive biology unit; (2) how does students' engineering design self-efficacy change throughout their participation in the unit; and (3) how did students experience CT and engineering design activities?