Instructional Development at a Time of Involuntary Changes: Implications for the Post-Pandemic Era
Bibliographic record
Abstract
Public health measures taken during the COVID-19 pandemic resulted in a series of involuntary changes in teaching and learning from 2020 to 2022, which could have promoted instructional development among instructors in postsecondary education. In this research paper, we used the four components in Kirkpatrick’s model of training evaluation—reactions, learning, behaviour, and results—to examine the data collected in summer 2022 from instructors of an engineering school of a public Canadian university. The analysis directed us to the following observations about the instructional development among faculty members in the engineering school during the pandemic. The teaching practices in most of the courses changed and most instructors consulted with resources for instructional support during the pandemic. The crisis during the pandemic serendipitously offered an unprecedented opportunity for instructional development toward online teaching. The instructional development is characterized by instructors’ reactions to their own online teaching experiences, positive attitudinal changes and skill development among some instructors with respect to online teaching, as well as the alternative teaching practices that emerged during the pandemic. However, this instructional development was passive and reactive in nature, and will not reverse the typical in-person course delivery in engineering. In addition, instructors in the engineering school accessed school-based resources for instructional support more often than university-based resources; and this resource access pattern will be likely to continue. Implications of these findings for instructional development are discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".