Exploring a Project-Based Training Model for Engineering Undergraduates Driven by Model-Based Systems Engineering
Bibliographic record
Abstract
Engineering undergraduate education is facing increasing challenges as emerging industries such as artificial intelligence, integrated circuits, and the low-altitude economy rapidly develop. Modern engineering practice involves highly information-intensive, interdisciplinary, and complex systems, placing higher demands on students’ systems engineering capabilities. However, existing undergraduate engineering education models often suffer from insufficient industry-education integration and project-based teaching that lacks methodological support. To address these issues, this paper proposes a project-based training model driven by Model-Based Systems Engineering (MBSE), in which MBSE serves as the core methodological framework rather than a task or result-oriented supplement. An MBSE-lifecycle-driven framework is adopted to restructure project-based teaching, encompassing requirement capture, system modelling, subsystem design and integration, and verification and validation. The proposed model emphasizes process-oriented learning and systems thinking. Furthermore, a new industry-education collaboration mechanism with deep enterprise participation and a multi-perspective evaluation system based on MBSE process artifacts are established. The proposed approach provides a systematic pathway for enhancing undergraduates’ ability to solve complex engineering problems and offers a replicable paradigm for engineering education reform and talent cultivation in emerging industries.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".