Research Paper: Where Do We Meet? Understanding Conference Participation in a Department of Engineering Education
Bibliographic record
Abstract
Abstract This Lessons Learned Paper focuses on understanding the different types of conferences where graduate students and faculty members in a department of engineering education typically present their work. The field of Engineering Education (ENGE) has been growing considerably in the last years, especially with the creation of several engineering education departments around the country. The Engineering Education community has developed several spaces in the United States(i.e. American Society of Engineering Education (ASEE) annual conference, ASEE regional conferences, Frontiers in Education conference (FIE), etc.) and internationally (The Australasian Association for Engineering Education (AAEE) conference, the Canadian Engineering Education Association (CEEA) conference, the European Society for Engineering Education (SEFI), and the Latin American and Caribbean Consortium of Engineering Institutions (LACCEI), among others), to continuously discuss the trends of the field. Nevertheless, engineering educators come from a very broad range of backgrounds, including people from several engineering disciplines, people from different educational backgrounds, people with different social science backgrounds, and even people with many different industry experiences. Hence, researchers have different roots that directly impact the way they conduct their research and the way they share their work. Many of them will present their work beyond the traditional venues created for engineering education, to better adapt to their disciplinary roots, or to develop connections in different fields required to move their research forward. The purpose of this paper is to better understand what type of conferences members of an engineering education department typically attend. Data is being collected quantitatively using a survey that is being distributed to everyone in a well-established engineering education department (40+ graduate students, 25 faculty members). Results will provide a better understanding of where members of the department share their work and where they attend to develop their academic connections. Results will help us to better understand the field and the diversity of backgrounds it entails. Results also will be especially useful for people new to the engineering education field that will be able to track not only the most traditional and recognized conferences in ENGE, but also become familiar with new venues that might be of interest for them based on their disciplinary background. This lessons learned paper will be presented as a lightning talk, however, it will include a portion of the conversation focused on the audience. To do that, we expect the audience to engage during the talk by using real-time feedback (e.g. Polleverywhere) to gather their preferences on attending conferences and see how those preferences contrast to the findings of our study.
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 | 0.024 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".