Improving Computational Thinking Competencies in STEM Higher Education
Nasrin Dehbozorgi, Mehdi Roopaei · 2024
Computational thinking (CT) is a key competency with a significant impact on students' academic performance in STEM fields. It empowers students to enhance problem-solving skills by decomposing problems, utilizing abstraction and pattern recognition, and employing algorithmic thinking to design solutions and build models. This is particularly important in STEM disciplines where logical reasoning is essential for addressing complex real-world challenges in academic and industrial settings. Given the increasing demand for professionals equipped with strong algorithmic thinking and problem-solving abilities in Industry 5, educational institutions are focusing on enhancing students' CT and problem-solving skills. This study presents an initiative conducted over the past two years at our institute to teach CT in a gateway course to students with different backgrounds in STEM fields. The approach involved designing specific learning modules on Abstraction, Decomposition, Pattern Recognition, and Algorithmic Thinking and integrating them into the LMS. After studying these learning modules, the students were exposed to specific assignments that required the application of related CT skills. Pre and post surveys were employed by using standard CT tests to measure the impact of the intervention on students' CT levels. The results indicated an improvement in students' perceptions of their mastery of CT. Academic course grades also showed an improvement, with increased A scores and reduced F grades post-intervention. This two-year study on improving CT skills has yielded promising results. Moving forward, the research aims to enhance the existing modules further and distribute them to a broader range of introductory-level STEM courses in other universities. This future direction aligns with the goal of expanding the impact of CT education and integrating it more widely into STEM curricula.