<![CDATA[Conceptual understanding in STEM disciplines represents one of the most persistent and consequential challenges in teacher education, where surface-level procedural knowledge without genuine conceptual depth produces teachers inadequately equipped to develop scientific and mathematical reasoning in their own students. Online STEM teacher education programs have proliferated rapidly across tertiary systems, driven by flexibility demands and widening participation agendas, yet their capacity to foster deep conceptual understanding remains unevenly documented and theoretically underdeveloped. This mixed-methods paper proposes a framework for improving conceptual understanding in online STEM teacher education through the purposeful integration of learning analytics dashboards within inquiry-based, interdisciplinary course designs. Drawing on conceptual change theory, cognitive load research, self-regulated learning models, and the emerging empirical literature on learning analytics in STEM contexts, the framework identifies four interdependent design levers: inquiry-structured interdisciplinary task sequences, analytics-informed formative feedback cycles, metacognitive scaffolding for self-monitoring, and institutional conditions governing equitable analytics implementation. A quasi-experimental study design involving 155 pre-service STEM teachers across two online cohorts provides the empirical architecture within which these levers are evaluated. Three quantitative tables present outcome data on conceptual understanding, engagement, and satisfaction alongside published benchmarks drawn from the STEM education and learning analytics literature. The paper argues that improving conceptual depth in online STEM teacher preparation requires simultaneous reform of instructional design, data-informed feedback practice, and the institutional governance structures that determine whether analytics tools serve learner development or reproduce the performance anxieties and equity disparities that poorly governed data systems characteristically generate. Practical implications address program designers, STEM educators, learning technology specialists, and institutional leaders.]]>
