Nursing faculty perceptions of preparation and support for effective online teaching
Bibliographic record
Abstract
Background: The steady growth of online education has resulted in the need for more faculty. Faculty have an integral role in creating the structure, processes and environment for effective student learning and, thus, require preparation and support to effectively perform this new role. As nursing programs expand capacity with online course delivery, the author found limited research on faculty perceptions of preparedness for teaching online. The purpose of this study was to explore faculty perspectives of teaching nursing content online in prelicensure baccalaureate nursing programs. In this article, the focus is on one specific aspect of the study, that is, the data that sought a deeper understanding of how prepared nursing faculty perceived they were and supports they needed for effective online teaching.Methods: The exploratory-descriptive, mixed-methods study design was based on document analysis, an online survey completed by 32 faculty (53.3%) and interviews with 16 faculty in a representative sample of 13 Ontario Colleges.Results: Institutional and faculty supports related to all best teaching practices. More faculty received an orientation to technology compared to the pedagogy of teaching online and experienced some challenges with these supports.Conclusions: Much more time was required for online teaching for which faculty should be compensated in workload assignments. Findings suggest that both technological and pedagogical training be integrated to faculty development programs and faculty be engaged in these programs prior to teaching online.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".