Experiencing Your Education: What Engineering Education Can Learn from Dialogue
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
What do engineers need to know beyond the textbook? Success as an engineer today also depends on the ability to hone skills such as team work, social intelligence and interdisciplinary collaboration, qualities that extend far beyond engineering itself. Dialogue education is one effective method being used in higher education to enhance student success, and it offers intriguing possibilities when paired with the curriculum for professional degrees. When students participate in dialogue education they not only sharpen professional communications skills, but also cultivate a richer understanding of the diverse perspectives which they encounter as they learn to engage constructively with the world around them. What can engineering education gain from dialogue education? In March 2011, the MetaKettle Project (Faculty of Engineering and Applied Science, Memorial University of Newfoundland), sponsored the "Dialogue Lab", a participatory workshop for graduate and undergraduate engineering students. The purpose of this workshop was to explore the ways that dialogue can be used as a practical and effective tool within the engineering profession in order to construct positive social, political, economic, civic and personal outcomes. This paper will report and reflect upon the results of the Dialogue Lab and examine what role dialogue can play in engineering education.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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