Bibliographic record
Abstract
In 2011, I conducted a research study that focused on the experiences of engineering educators who were implementing innovative pedagogies such as problem-based learning (or its variations) in undergraduate engineering education on a consistent and committed basis. The intent of the study was to identify not only the tensions encountered but also the management of tensions that arose when implementing these pedagogies. I specifically sought out this group of educators on the assumption that their belief in the benefits and outcomes of PBL outweighed the challenges that they faced at a classroom and system level when they, in essence, turned away from what may be considered a ‘traditional’ approach to engineering education.A survey was designed to capture data relevant to the research questions around implementation of PBL and tensions encountered. The data collection (1 month) period resulted in 313 valid survey responses who met inclusion criteria. Sixty-five engineering educators were interviewed on their teaching practices and management of tensions encountered when implementing PBL. At the end of the data collection period, I was left a sense of admiration for these educators who, despite having to address predictable and unpredictable tensions because of their pedagogical beliefs, maintained a course that they believed would best serve their students and society. So, between March 1 and July 29, 2011, a follow-up question was sent to all the educators who had been interviewed (n=65; response rate = 100%) and to those who not interviewed but had provided contact information (n=172; response rate = 33%). They were asked the following question: For an engineering educator wanting to implement PBL into their teaching practice, what words of wisdom (lessons learned) would you offer them (3‐5 bullet points)?The benefit of aggregating this sort of information may prove very useful for engineering educators and educational institutions planning the implementation of innovative pedagogies such as PBL.
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 | 0.080 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".