Exploration of Ideological and Political Teaching Reform in the "Project Management of Engineering" Course Based on the OBE Concept
Bibliographic record
Abstract
Based on the concept of OBE (Outcome Based Education), the ideological and political teaching reform of the " Engineering Project Management" course was explored under the background of electrical engineering and automation specialty in this study. Centering on the requirements of engineering education accreditation and in combination with the characteristics of this specialty, the learning outcomes of the courses were clarified, and the ideological and political elements were integrated into the PMBOK (Project Management Body of Knowledge). It included teaching links such as project integration management, scope management, time management, cost management, quality management, human resource management, communication management, risk management and procurement management, thereby cultivating the engineering ethics, social responsibility and craftsmanship spirit of students. By optimizing teaching content, adopting innovative teaching methods (e.g., case-based teaching and immersive case analysis), and improving a diversified evaluation system, the coordinated development of knowledge imparting and value guidance can be achieved. This study provided a valuable reference for cultivating electrical engineering talents with both moral integrity and professional competence through teaching reform exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".