A Cloud Learning‐Analytics Framework for Multi‐Tier Engineering Education: A Mixed‐Methods Implementation Case Study Across Three Avionics Courses

Computer‐application‐mediated learning analytics can connect simulation, controlled measurement, and field practice, but engineering‐course evidence is fragmented across instruments and units of analysis. This article contributes (1) an adaptation‐oriented four‐stage spiral curriculum with a six‐step weekly micro‐routine and (2) an implementation‐oriented cloud learning‐analytics framework for organising evidence. The curriculum ran for one academic year across three courses as a bounded single‐site case; the framework was specified but not analytically validated. Only Radar Systems and Microwave Engineering had traceable outcome summaries. An enrolment audit identified 49 records for 45 unique students; after one withdrawal, 48 completion records represented 44 students. The quantitatively driven convergent design combined course‐level summaries with 42 archived Radar Systems text entries (43 coded segments) as embedded qualitative evidence. The institutional report documented a Radar Systems knowledge improvement of at least 20% on the pre/post test and a pass‐rate change from 63% to 81%; Microwave Engineering recorded a project mean of 82.5/100 (4/4 groups ≥ 80). Feedback concerned gamified engagement, peer learning, reduced boredom, memory reinforcement, and active learning. Kahoot! records re‐exported during revision show a same‐cohort improvement of +16.9 percentage points (Hedges g av = 1.08) for the 22 students attending both first and final quiz sessions—not an equivalent‐form pre/post gain, because sessions covered different units; an alternative pairing of 31 students yields −4.8 points (non‐significant). Otherwise, item‐level responses, paired knowledge‐test records, and rater‐level scores were not retained, so reliability coefficients, effect sizes, and confidence intervals are not reported. The results support implementation feasibility and plausible learning mechanisms, not causal efficacy.