Entering the Discipline of Engineering Education Research: A Thematic Analysis
Bibliographic record
Abstract
In this study, we used classical grounded theory and thematic analysis to develop a framework to help us understand the process that academics go through to become engineering education researchers.As a data source, we accessed the publicly available interview transcripts from the Cambridge Handbook of Engineering Education Research: Updated Perspectives (CHEER-UP) 2020 virtual summer seminar.In this series of 15 seminars, 32 CHEER authors engaged in one-hour discussions to elicit their current views on the topic highlighted in their chapters.As part of the introduction to each seminar, the authors answered why and how they entered the field of EER, which we used for our analysis.Using NVivo 12, we administered a line-by-line coding of the interviews using inductive thematic analysis, identifying themes that helped us answer our research question.We identified five main themes: Engineering Culture, Opportunity, Education Knowledge Community Involvement, and Desire to Right Wrongs.The individual themes identified here are aligned with and supported by publications in engineering education and other disciplines.The central ideas of our findings are two-fold.First, an Opportunity is often the catalyst for the boundary-crossing between the disparate disciplines of engineering and education.Second, having an intrinsic motivation (i.e., Desire to Right Wrongs) and the external support of Community Involvement are crucial to help the researcher continue to thrive and explore within this dual-discipline in which boundary-crossing is endemic.
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 | 0.115 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".