Quantum physics meets computational thinking: accessing quantum mechanics with Python-based computational exercises
E Kuusisto, Antti Lehtinen, Pekka Koskinen · Physics Education · 2025
Abstract Learning quantum mechanics (QMs) poses unique challenges due to its abstract, unintuitive nature and departure from classical physics paradigms. In this context, computational methods hold potential not only for improving conceptual understanding but also for addressing the motivational difficulties often encountered in learning QMs. This study investigates how computational Python exercises impact students’ experiences and conceptual understanding in a massive open online course designed for high school and early undergraduate students. Data was collected using an online questionnaire that probed the understanding of QMs concepts and computational methods. The 48 participants were randomly assigned to two groups that completed the questionnaire and the computational exercises in a different order. Students’ answers were studied using content analysis, rubrics, and statistical methods. The results suggest that while computational methods offer valuable means for concretizing abstract QM ideas, attention must be paid to how computational models are presented, particularly for students with limited computational background. This study provides preliminary evidence that a short, carefully designed computational intervention can positively influence students’ views on computational thinking.