Unpacking Trends in Students' Analogical Reasoning with Unsupervised Machine Learning

Analogical reasoning is a key component of scientific understanding, enabling students to connect new concepts to familiar experiences. This study investigates the extent to which around 900 students in a large enrollment physics course leverage analogies while explaining the Morse potential curve in language suitable to a second grader and in terms of their preferred everyday contexts. This talk presents the challenges in analyzing diverse, complex, and detailed student-generated analogies using K-Means clustering, particularly while relying solely on traditional clustering metrics such as silhouette scores and cluster distribution. The effectiveness of accounting additional cluster metrics, such as essay centroids and representative words, along with standard qualitative approaches in a systematic mixed-methods approach to mitigate the challenges are also discussed. The resultant clusters and sub-clusters highlight the broad emergent categories of the concepts associated with the curve and corresponding analogies employed to communicate them. Contrary to current understanding of students' analogical reasoning in physics, we observe that students often employed similar analogical contexts in effectively communicating diverse conceptual ideas.