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Reform documents advocate for innovative pedagogical strategies to enhance student learning. A key innovation is the integration of science and engineering practices through engineering design (ED)-based physics laboratory tasks, where students tackle engineering design problems by applying physics principles. While this approach has its benefits, research shows that students do not always effectively apply scientific concepts, but instead rely on trial-and-error approaches, and end up their way to a solution. This leads to what is commonly referred to as the —that students do not always consciously apply science concepts while solving a design problem. However, as obvious as the notion of a may appear, there seems to exist no consensus on the definitions of and , further complicating the understanding of this gap. This qualitative study addresses the notion of the design-science gap by examining student groups’ discussions and written lab reports from a multiweek ED-based undergraduate introductory physics laboratory task. Building on our earlier studies, we developed and employed a nuanced, multilayered coding scheme inspired by the Gioia Framework to characterize and . We discuss how student groups engage in various aspects of design and how they apply physics concepts and principles to solve the problem. In the process, we demonstrate the interconnectedness of students’ design thinking and science thinking. We advocate for the usage of the term as opposed to to deepen both design and science thinking. Our findings offer valuable insights for educators in design-based science education. 

We analyzed the essays that were written on various topics in an introductory physics course using two unsupervised machine learning algorithms. One of them was Latent Dirichlet Allocation (LDA). This algorithm is used for extracting abstract topics from a collection of text documents. The other algorithm was Non-negative Matrix Factorization (NMF). It is used for similar purposes but also in other domains such as image recognition. We applied these two algorithms to the dataset that consisted of N=683 student essays. Although there were some built-in, important differences between LDA and NMF, they both found similar topics in our data by large. This offers instructors a promising and productive way of accessing useful information about their students' written work, especially in large-enrollment classes. 

By integrating physics laboratories with engineering design and computer science, students apply physics principles to ill-structured and complex problems, engage in knowledge transfer, and gain interest in STEM. The introductory physics labs at Purdue have been updated to include engineering design and computer science principles that ground physics in authentic problems. Integrated labs have been evaluated using student perception post-surveys, student course performance, interviews, and case-study observations. Preliminary results indicate that integrated physics labs promote transfer, enhanced metacognitive skills, student interest, and motivation.