How CETYS Engineering is Preparing Its Students for Success
Bibliographic record
Abstract
The Center for Technical and Higher Education (CETYS University) is a private institution of educational excellence, born in 1961 in Baja California under the auspices of a group of visionary entrepreneurs committed to education. CETYS Engineering recognized in the Spring of 2016 that it needed a formal organization to provide third-party, external feedback for the advancement of the CETYS College of Engineering. An Engineering Advisory Council (EAC) was formed that Spring.At the May 2017 meeting of the EAC it was asked:What competencies are needed for the engineer of tomorrow?How is CETYS preparing its engineering students to meet this challenge?These two simple questions lead to a year-long study conducted by the EAC. The results of this effort are presented in this paper. Key findings are as follows:With a broad industry sample size of 42 we determined what are the most important Domains of Attributes, Skills and Abilities an engineer needs to possess.Using this Domain prioritization we then learned “What are the biggest Competency Gaps?” that industry is finding in engineers today.Using this Gap analysis we then looked at the CETYS curriculum and determined that almost all of the gaps are overtly addressed in multiple classes.Finally, the Engineering College is taking action to determine if those not formally addressed can be addressed in the educational experience.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".