Modelling Approach of an Innovation Process in Engineering Education: The Case of Mechanical 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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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