Increasing student engagement with graduate attributes
Bibliographic record
Abstract
It is widely recognised that there is a need to develop a range of generic graduate attributes in engineering students. In order to develop these attributes, universities have employed a number of strategies, including staff development and the adoption of non-traditional teaching methods. However, students also need to have a clear understanding of the meaning of the attributes and why they are important in a professional engineering context. Consequently, student engagement with graduate attributes is also an important factor in their successful development. In this paper, an efficient approach for achieving this is introduced and an example application presented. The proposed approach revolves around a classroom exercise as part of which groups of students discuss and rate the relevance of a set of graduate attributes from the perspective of a practising engineer, about whom they have been provided with relevant background information. Next, the ratings (relevancy scores) given to each of the attributes by the student groups are compared with those provided by the actual engineers, followed by discussion about any similarities and differences between the scores. In addition to increasing student engagement with graduate attributes and student understanding of their importance and relevance, this exercise also provides students with an insight into what “real” engineers do, and what students might expect to be doing once they graduate. Such an exercise was conducted during a single 50-minute tutorial session in the course Environmental Engineering II as part of the Civil & Structural and Civil & Environmental degree programs at the University of Adelaide. A student survey indicated that the exercise was successful in increasing student awareness of the existence of, the need for and the importance of graduate attributes, as well as helping students to gain a better understanding of their meaning.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".