Developing a new Engineering Technologist career pathway from First-Year Engineering
Bibliographic record
Abstract
Successfully completing an engineering degree often requires at least four or five years of intense study by students, and many of these students start their programs with a limited sense of what an engineer is or does. During their journey, some students gravitate towards a blend of practical application and theoretical knowledge of engineering principles; some, for diverse reasons, may be unable to invest the time required for an engineering degree. For these students, an engineering technologist career, which focuses on application and implementation, may be more appropriate. Leveraging the common first-year engineering curriculum recently launched in British Columbia, Vancouver Island University has developed and implemented a new, generalist, Integrated Engineering Technologist diploma (ITED) that combines civil, mechanical, and electrical principles, and provides a career pathway for those students who start engineering studies but choose not to continue with the degree. This paper will focus on the development of this new diploma, while a subsequent paper will evaluate its implementation. The three development phases of the IETD were:1. Identifying key program graduate attributes,2. Developing the program structure and delivery modes, and,3. Creating the detailed course content. Within the first phase, an inventory of desired skills was obtained through broadly distributed surveys, direct engagement with industry, professional, and government groups, and evaluation of future needs within the technologist profession. This paper will outline the methods used to collect this data, and the process by which this data was developed into a thematic collection of higher order skills and attributes. Through this iterative consultation process, a technologist credential with a broad, non-specialized disciplinary focus was found to best meet the identified skills gap and need. The second phase consisted of a study of engineering, technologist, and technician programs to evaluate best practice. This paper discusses the cohort model that was ultimately chosen, and the highly modular approach used by the instructor team. Each four-week module consists of up to five courses run in parallel, where individual learning topics are treated collectively and strategically sequenced to best facilitate learning. A summative assessment evaluates learning at the end of each module. In the final phase, which is on-going, specific course content is being developed, including lab-based and field activities, classroom-based learning, and project work. Examples of this work and their motivation are provided.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".