INNOVATIVE TEACHING METHODS AND ENGINEERING EDUCATION RESEARCH
Bibliographic record
Abstract
Problem-based learning approaches have been deemed by research literature to be an optimal approach to develop engineering graduate competencies and attributes. While project-based capstone courses tend to be the norm, PBL has a lesser although highly recommended presence in the early years of an engineering program. With early year implementations of innovative pedagogies, engineering educators who persist in their PBL implementations encounter tensions at various levels and are required to devise strategies to manage the tensions.This qualitative study focused on the variation in engineering educators’ ways of experiencing tensions in PBL implementations, as well as how they managed the tensions (n=14). In the specific context of the first two years of undergraduate engineering education, the research questions were (1) based on their teaching practices, what are the predominant tensions encountered by engineering educators? (2) What are the qualitatively different ways in which engineering educators experience tensions with a PBL implementation in their teaching practices? (3) How do engineering educators manage these tensions?Results revealed tensions at both the classroom and system level. Examples of a classroom tension included the transitioning of students not only into engineering but also into PBL-oriented learning environments. System-level tensions included a misalignment in the perceived value assigned to teaching by the individual instructor and the organization.For engineering educators considering the implementation of PBL, this study offered not only insights into potential tensions, but also the management strategies used to mitigate the tension. Implications for administrators, faculty development specialists, and curriculum designers are also discussed.
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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 | 0.044 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".