curricular-analytics
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- PaperJournal of Learning Analytics21 Jun 2026
Practitioner-Informed Learning Analytics Metrics for Measuring Curricular Complexity for Transfer Students
David Reeping, Dustin Grote, Julie Christensen, Sulabh Khadka et al.
This study expands the Curricular Analytics framework by proposing three new metrics—inflexibility factor, transfer delay factor, and credit loss—to better measure curricular complexity for transfer students. Through focus groups with 38 transfer professionals, the metrics demonstrated strong construct and content validity for capturing challenges faced by transfer students.
Original abstract
This study expands the concept of curricular complexity as defined in the Curricular Analytics framework (Heileman et al., 2018) by providing qualitative evidence of the suitability of three new metrics, concerning timing of course offerings, extended time-to-degree, and credit loss, that more adequately address curricular challenges encountered by transfer students. Curricular Analytics is a method for analyzing a curriculum that enables practitioners and researchers to quantify and systematically analyze the impacts of course sequencing in a plan of study on student outcomes. However, the original conceptualization falls short of capturing the substantive challenges faced by transfer students who enter an undergraduate program at various points in the curricular sequence. This study was guided by the following research question: “How do three new measures of curricular complexity (i.e., inflexibility factor, transfer delay factor, and credit loss) align with transfer professionals’ perceptions of curricular barriers for transfer students?” Using a grounded theory approach, we conducted seven focus groups with 38 transfer professionals across the United States. We presented these transfer experts with each new measure and prompted them to reflect on its validity based on their experiences supporting transfer students. We found transfer professionals resonated strongly with all three new metrics, suggesting strong initial construct and content validity.