Dynamic decision-support for engineering course evaluation and improvement: a case study of the electric machinery...

Engineering education accreditation emphasizes that course evaluation should enable continuous improvement. However, existing evaluation models are often constrained by static characteristics, single-dimensional assessment, insufficient predictive capability, and a disconnected closed-loop optimization mechanism. To address these challenges, this study develops a dynamic educational decision-support framework encompassing evaluation, diagnosis, prediction, and optimization. Using five-period data from 2021 to 2025 (n = 882) for the Electric Machinery and Drives (EMD) course, the framework is empirically demonstrated the proposed framework on a single-course longitudinal dataset, establishing 16 dynamic evaluation indicators covering both student and instructor dimensions. The framework integrates temporally adaptive weighting, correlation-aware multidimensional evaluation, trend-aware shortcoming diagnosis, and uncertainty-aware prediction. Specifically, sliding-window entropy weighting captures temporal changes in indicator importance, Mahalanobis-distance TOPSIS accounts for inter-indicator correlations, Markov prediction with Bootstrap confidence intervals estimates future state distributions, and D-SRC is formulated as a trend-aware extension of the Shortcoming Repair Coefficient to support proactive teaching intervention. On the basis of the three traditional dimensions (importance, deficiency, and student differentiation), the short-term evolutionary trend of indicator scores is introduced to realize bottleneck identification with both status diagnosis and prospective early warning. A three-dimensional, nine-measure optimization path is then formulated. The five-year analysis showed that the weights of technology-driven and ethics-related indicators changed substantially, with b10 and b15 increasing by 228% and 150%, respectively. M-TOPSIS provided correlation-aware evaluation and increased the SD-based relative dispersion of closeness coefficients by 10.7%, while Markov prediction with Bootstrap confidence intervals generated a short-term baseline projection of the 2026 excellent rate. After D-SRC-guided interventions for b15, b10, and b8, the corresponding scores increased by 7.7, 7.3, and 6.3 points, and the overall excellent rate increased from 24.1% to 39.6%. The proposed framework provides a decision-support tool for engineering education continuous improvement. By incorporating temporal trend information, D-SRC extends static shortcoming diagnosis toward prospective early warning and supports targeted intervention planning. The three-dimensional, nine-measure strategy further links diagnosis with teaching improvement, supporting the transition from static course assessment to dynamic, feedback-oriented educational decision support. Future studies should validate the framework across additional courses and institutions.