Exploring a Project-Based Training Model for Engineering Undergraduates Driven by Model-Based Systems Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it