Educational research impacting engineering education
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
In recent years research studies into critical factors for learning in engineering education have started to emerge in Europe and worldwide. One manifestation of this is the formation of the SEFI working group for Engineering Education Research (EER) at the 36th annual conference in Aalborg last year. We hope this special issue will serve as another indication and realisation of the emerging field of engineering education research. For this issue we were searching in particular for papers with a qualitative approach and which had a firm foundation in educational theory with a strong connection to, or a strong potential for affecting, the praxis of engineering education. The interest to get a paper published in this special issue greatly exceeded our expectations and we received 38 paper proposals. In this introduction we will not attempt to give another summary of the 10 papers finally published – that is given in the abstracts of the papers. Rather we will try point out some common topics and make some cross-paper comparisons. We believe it is necessary in educational research and in engineering to use quantitative as well as qualitative approaches. However, there is a tension between qualitative
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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