Productive tensions? Analyzing the arguments made about the field of engineering education research
Bibliographic record
Abstract
Abstract Background A body of literature has arisen analyzing and legitimating the emerging field of engineering education research (EER). Using concepts from the sociology of knowledge, EER can be described as a region because it has relationships both to other academic fields and to its field of practice. Of interest is the strength of boundaries between these fields, described by the sociologist Bernstein's concept of classification. Purpose/Hypothesis This study addresses the research questions: (1) How, when and by whom are arguments made to strengthen or weaken the boundaries, first between EER and other academic fields and second between EER and engineering teaching? (2) How do these arguments change across time and national contexts? Design/Method Drawing on a survey of 21 EER experts, this sociological discourse analysis focuses on a purposive dataset of 17 papers from 2000 to 2020. Results The study identified three main arguments in this literature, favoring: (1) strong classification (a singular in sociological terms); (2a) a region linked outward to teaching practice; and (2b) a region linked inward to other social science disciplines. Conclusions The argument for EER as a strongly classified field has served value in establishing legitimacy and associated resources in some contexts but has not yet delivered a unique knowledge base for such legitimation. An alternative framing holds together the productive tension between two directions in which EER as a region can face: Looking inward to parent disciplines for theoretical and methodological direction and looking outward to the world of practice for meaningful problems to guide its studies.
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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 | 0.164 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| 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".