Using network and qualitative analysis of expert concept maps to establish learning progressions in quantum sensing

Learning progressions (LPs) are sequences of knowledge and skills illustrating how students can progress from fundamental ideas to higher-order concepts. Identifying such LPs at the course level provides educators with a clear teaching pathway, outlining where to begin and how to scaffold instruction toward more advanced topics. Incorporating emerging topics such as quantum sensing into the existing curriculum requires defining these progressions, however, it can be challenging due to the lack of established curricula and textbooks. To support the development of LPs, we used an expert knowledge elicitation methodology that uses concept maps to capture the deeply structured mental models of experts. Our analysis involves standardizing across multiple concept maps and categorizing the nodes into a hierarchical family of categories to build an integrated network map. The nodes are also tagged based on the type of courses they appear in, which enables us to identify course-specific progressions. By tracing chains within the network map, following connections toward higher-order nodes, we show how LPs can be identified for different undergraduate quantum-related courses (e.g., modern physics, quantum mechanics, or quantum computing).