From Effective Teaching Practice to Engineering Education Research: Where are the Gaps, and How Can the Gaps Be Bridged?
Bibliographic record
Abstract
BACKGROUND & CONTEXT Within the engineering education communities, there appears to be a wide gap between teaching practice and engineering education research (EER), particularly exhibited in the distinct research-practice divide in the knowledge production processes. PURPOSE OR GOAL The purpose of this paper is to use a sample of published papers on teaching practice to illustrate how the gap between effective teaching practice and EER may be bridged. APPROACH OR METHODOLOGY We first draw upon three conceptual frameworks or models for Scholarship of Teaching and Learning for insights. Then we examine a sample of 54 papers on teaching practice published in 2024 by Advances in Engineering Education, the European Journal of Engineering Education, and the International Journal of Engineering Education. In our analysis, we marked down the data collection methods in those example papers, and annotated the research designs they employed, using methodological terminologies as per research design literature. OUTCOMES Our review shows that most of the studies in the example papers used one-group pre-experimental research designs, which exhibit a lower level of methodological rigour but present less ethical challenge in implementation than quasi-experimental and experimental designs. We have identified three strategies for engineering educators to turn their teaching practices into research papers: utilizing various research designs, applying various genres to academic writing, and embracing diversity and rigour in studies on teaching practice. CONCLUSIONS This paper will help empower academics in engineering to conduct research on teaching practice, thus enhancing the research capacities of the EER communities.
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 | 0.109 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.044 | 0.046 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".