Art-Inspired Pedagogies in Engineering Education - Using Comics, VR/AR, Gaming, and Music in Engineering Education
Bibliographic record
Abstract
In recent years, there is growing recognition in engineering education that creative, humanistic, and transferable skills such as emotional intelligence, ethical leadership, and teamwork, are essential to students' success, thriving, and contribution in university and beyond (Jarrahi et al., 2023;Lappalainen, 2015;Rottmann et al., 2015; World Economic Forum, 2020).However, most engineering students are used to rigorous curriculums that emphasize technical development, with little opportunity to experience and explore creative and humanistic subjects and to develop ethically and holistically (Cech & Sherick, 2015;Riley, 2008).Moreover, many engineering students who are used to highly reductionist and analytical thinking find it difficult to engage with "softer" learning and may experience lowered motivation in these subjects (Badenhorst et al., 2020).Underlying these challenges is a cultural belief within engineering that sees the rigour and rationality of math and science being superior to the creativity and emotionality of arts and humanities (Riley, 2017;Wisnioski, 2015).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".