An analysis of engineering educators’ understanding of complementary studies courses using the repertory grid technique
Bibliographic record
Abstract
Accreditation bodies such as the Engineering Council of South Africa and the Canadian Engineering Accreditation Board have a group of courses that fall under the umbrella of Complementary Studies. This term is used to describe a set of engineering courses that include knowledge areas other than the more common mathematical sciences, natural sciences, engineering sciences, design and synthesis, and workintegrated learning. Studies have shown that engineering educators sometimes view these courses negatively. They are seen as distracting the focus of the students on the so-called technical courses, which the educators feel are more important. This paper reports on a research study that explored the way that engineering educators make sense of complementary studies courses within an industrial engineering curriculum. The repertory grid technique was used to explore complementary studies courses when compared to other engineering courses within the same curriculum. The relationships between elements and constructs in the grids were analysed using the repertory grid techniques of principal component analysis and cluster analysis. What became clear was that while most of the educators interviewed did recognise complementary studies courses as different to courses considered as core or technical, what made them different was very unclear. Each educator had a very different conception of what defines, differentiates or constitutes a complementary studies course. This range of variation may go some way to explaining why complementary courses seem out of place in engineering programs by educators and students alike.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".