Course design shapes student experiences in online engineering courses, yet translating design artifacts into analyzable data remains a methodological challenge. This full methods paper introduces a systematic and adaptable framework that operationalizes an adapted course quality scorecard into four analytic indices representing engagement, scaffolding, accessibility, and alignment. Behavioral data from institutional learning platforms provide observable proxies of enacted instructional design, complementing the scorecard and enabling triangulation between automated behavioral signals and human design assessments. A demonstrative case study illustrates the pipeline and identifies boundary conditions that influence the interpretability of the indices, including class size, assessment structure, and course format effects. The framework emphasizes transparency, reproducibility, and adaptability, offering a methodological foundation for design-informed investigation of student outcomes across diverse institutional contexts.
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