Working with Others – An Analysis of Collaborative Research Likelihood
Bibliographic record
Abstract
Background: While diversity and teamwork is valued increasingly in engineering education in particular and in our society in general, we intend to explore the quality and quantity of academic collaborations to understand the current state-of-art of collaborative research projects within engineering education. Purpose: Our long-term objective is to understand why collaborations occur and what makes it more likely to occur. For this work-in-progress paper, we observed the trends of collaborations within Engineering Education Research and observed whether and how collaboration behaviour changed, in measurable ways, during the COVID-19 pandemic. We also observed whether three seems to be a relation between the quality of research within Engineering Education Research because of collaborations. Methodology: We explored and compared the articles published in all sections of the annual conferences of American Society of Engineering Education, Canadian Engineering Education Association, and European Society for Engineering Education from 2017 – 2022 and publications in the Journal of Engineering Education from years 2017 – 2022. Results: Our preliminary results have shown that the quality of Engineering Education research seems to improve only slightly with collaborations. Additionally, we have observed that the percentage of collaborative research projects differ in the five places that we have explored. Conclusions: The long-term goal of this research is to identify the kinds of projects that can be done collaboratively and also identify gaps in collaborative research so that other researchers can use that information to design more collaborative projects. This initial work is to estimate recent levels of collaboration using author counts.
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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 | 0.154 | 0.545 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".