Professional Skills Needed by Our Graduates.
Bibliographic record
Abstract
Accreditation agencies have outlined professional skills that should be possessed by engineering graduates. The question this paper addresses is “What professional skills do our recent graduates actually use and value as being important?” Young engineering professionals, as well as professionals in many different professions, were asked to identify the importance and frequency of use of 23 “professional skills.” The results were that the top skills of importance and frequency of use were verbal communication, written communication, time management, problem solving, decision making, teamwork, critical thinking, self-confidence, initiative, building trust, and stress management. Those that were important and used weekly to occasionally in three months were social awareness and management of relationships, self-awareness and management of emotions, leadership, lifelong learning, analysis (classification, series and patterns, and consistency), self-assessment, empathy, creativity, intercultural understanding, research, change management of self and others, and chairing meetings (being a chairperson). For the sample of 33 who graduated with engineering degrees, their results showed little difference from the total group of 104 respondees. Some suggestions are given about what we can do in the classroom to help our students gain these skills.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".