Establishing learning objectives based on expert knowledge networks in emerging physics fields

A learning objective (LO) is a measurable description of what students should be able to do following instruction. Clearly defined LOs are essential for backward design of curriculum and assessment, but challenging to establish in contemporary areas such as quantum technology. In this work, we demonstrate a data collection and analysis pipeline for eliciting expert knowledge in the context of quantum sensing, to systematically identify LOs. We use the concept mapping methodology to capture the deeply structured mental models of experts. Our analysis involves standardizing and integrating concept maps from over 20 experts, and organizing the nodes into ontological categories (such as entity, phenomenon, application, etc.) to construct network maps. By pairing our node categorization hierarchy with different levels of Bloom's taxonomy, we show that well-defined LOs can be developed for quantum sensing concepts that are appropriate across a range of undergraduate quantum-related courses. For example, the network map can be used to identify the core concepts underlying a diverse range of sensing platforms. Based on these concepts, we can derive LOs that bridge foundational topics to sensing applications, and position each objective within Bloom’s taxonomy.