Increasing student engagement with graduate attributes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it