Modelling Approach of an Innovation Process in Engineering Education: The Case of Mechanical Engineering
Bibliographic record
Abstract
Nowadays, the concept of innovation is omnipresent in most political discourses as well as in technological, socio-economic and scientific development plans. Innovation is essential for solving complex problems in new ways, which can lead countries to development and prosperity. The realisation of an innovation is not the result of a random act, but the product of a multidisciplinary process, rich in methods and scientific and technical tools, using materials and human resources. These human resources, in particular engineers, must possess both technical and soft skills that strengthen their capacity to innovate, and which have been in continual development since the initial training phase. Innovation is at the heart of engineer training concerns and requires management and structuring according to a well-defined process.The objective of this paper is to present the approach followed to define an innovation training process model for engineers through Project Based Learning (PBL). In this work, we have identified the main components of our process through a combination of data from the literature review and the results of an empirical study. Innovative projects in the field of mechanical engineering, carried out by future Moroccan engineers, were studied in depth. The results of the study enabled the identification of the different elements characterising the process of carrying out an innovative project such as the inputs, outputs, control milestones and resources required for the implementation of innovative educational projects in this field. These elements were supported by semi-directive interviews to form the basis of our systemic modelling.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".