Assessment of transition from mechanical engineering to mechatronics engineering in turkey
Bibliographic record
Abstract
This paper gives an assessment of the transition from the mechanical engineering curriculum to the mechatronics engineering curriculum in Turkey. It looks at the requirements for the transition and analyses the approaches adopted by Turkish universities. To achieve this, the study provides a review of the mechanical engineering departments and the proportion of mechatronics courses taught within these departments. As presented in the paper, some universities prefer a separate department for mechatronics engineering; others introduce optional courses, while the rest replace some core modules with mechatronics engineering type courses. Therefore, this work classifies the universities into three groups. In addition to Turkish universities, some selected cases of universities from Asia, the USA, Canada, and Europe are also included as examples of each identified approach, thereby providing the necessary background for comparison. The comparative study reveals that there does not seem to be a definitive approach to updating a mechanical engineering curriculum or a mechatronics engineering curriculum with any clearly defined structure. Nevertheless, the proportions of mechatronics courses in mechanical engineering curricula in Turkish universities indicate that the required measures seem to have been taken in most of the cases. In this study an attempt was also made to identify the problems that Turkish universities are facing in mechanical engineering education and some suggestions were made to overcome these difficulties to improve the quality of such education in Turkey. The paper concludes with a general suggestion that consists of a set of solution models that may allow a smooth transition from a mechanical engineering to mechatronics engineering curriculum.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".