The Relationship between Instructors, Academic Leaders, and Educational Developers in the Development of Online Teaching Capacity
Bibliographic record
Abstract
Online learning is expanding rapidly among higher education institutions; this requires faculty members to be prepared to teach in online environments. While some may have experience with online teaching and learning, others may feel the need to enroll in educational development programs to help them develop their competence and confidence in teaching online. Using an instrumental single case study approach, this research sought to understand the ways in which instructors from a professional faculty of a western Canadian university developed their capacity to teach online. The following question guided the inquiry: how does the relationship between instructors, academic leaders, and educational developers influence the process of online teaching capacity building in a professional faculty at a western Canadian university? Participants of this study included instructors and academic leaders of a professional faculty, and educational developers working at the university’s centre for educational development. Data were collected using interviews, surveys, and document analysis. Three key findings emerged from the analysis of the data: 1) how technological and pedagogical considerations affected online teaching and its capacity-building processes; 2) initiatives used by instructors to build their online teaching capacity, and 3) factors that influenced the processes for online teaching capacity building. These findings point to the need for synergetic relationships between online instructors, academic leaders, and educational developers for the development of online teaching capacity-building processes and practices that create the conditions for meaningful student learning.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".