COVID-19: A MOTIVATOR FOR CHANGE IN ENGINEERING EDUCATION?
Bibliographic record
Abstract
Despite recent research and initiatives, learner-centered instructional practices have not made their way into post-secondary Science, Technology,Engineering and Math (STEM) classrooms, even though there is clear evidence showing the benefits include increased grades, higher student engagement, and deeper learning. STEM educators rank the barriers associated with active learning higher than their colleagues in other disciplines, and identify the inability to cover all the content as a key factor in their decision to adhere to didactic practices. Insights and instructional strategies and methods garnered from teaching-related faculty development opportunities are often tried, but their use is not generally sustained unless a personal experiencedrives that change in practice. Unquestionably, COVID-19 has had an immediate, global impact on higher education. Educators have been forced to alter their teaching practices to accommodate the switch to remote learning. Most Teaching and Learning Centers offered myriad workshops to facilitate this change. This quantitative study set out to determine if COVID-19 precautions created the personal experience necessary to initiate a change in STEM teaching practices. Using educator-related threshold concepts as a framework, it analyzed institutional registration records to determine the type of faculty development opportunitieschosen by engineering educators, and the extent to which they participated in those related to learner-centered instructional practices for remote delivery.Analysis shows that engineering educators participated proportionally less than their colleagues in other disciplines, and there is an indication that the pandemic may facilitate an ongoing change in the teaching practices of engineering educators. Opportunities for enhancing faculty development practices for engineering educators are proposed.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".