New Lenses on Learning Design: Course Types and Indicators from Large-Scale Moodle Data
Ekaterina Soroka, Niels Seidel, Marcus Specht, Kamila Misiejuk
Although learning design (LD) plays a central role in shaping online courses, large-scale empirical evidence on how courses are actually structured remains scarce, and existing analytics work has characterised courses through student engagement with specific learning activities rather than through indicators of the design itself. This study addresses both gaps through an analysis of learning management system (LMS) data from 606 courses at a distance university. Using LMS data, we first applied dimensionality reduction and clustering to a structural variable set, from which three distinct LD types emerged empirically: Compact, Expansive, and Interactive and statistically characterised how their design elements differ across disciplines, activity load, and interfaces. Building on this typology, we derived three LD indicators, activity-mode balance, structural clarity, and analytics readiness, each combining multiple LMS-derived variables into higher-order constructs that target a different aspect of LD. All three indicators differentiated significantly across the LD types and added pedagogically meaningful dimensions for interpretation. These contributions advance the operationalisation of LD in technology-enhanced learning research by demonstrating that higher-order LD indicators can be derived directly from routinely collected LMS data, and offer empirical insight into how LD manifests at scale within a large distance university.
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