An Exploration of Faculty Perspectives of Online Teaching in a Sample of Collaborative Baccalaureate Nursing Programs in Ontario Colleges
Bibliographic record
Abstract
This thesis examined online teaching in the Ontario college sector in prelicensure collaborative baccalaureate nursing programs with the purpose of exploring and understanding best practice implementation and the nature and appropriateness of curriculum content for online delivery in nursing programs, as perceived by the faculty who have taught or are teaching courses online. The overarching question for this study was: What are the perceptions of participating faculty regarding the nature, challenges and strengths of teaching online course content in prelicensure collaborative baccalaureate nursing programs, and what are the implications for online course delivery? I used an exploratory-descriptive design and constructivist lens and pragmatic worldview with a mixed-methods data collection methodology to answer the research questions. The theoretical framework was rooted in constructivism as a teaching approach. My study included a representative sample of 13 English language Colleges in Ontario that offer prelicensure collaborative baccalaureate nursing programs. Participants were full-time and part-time nursing faculty and program coordinators in these programs. Data were collected through document analysis, an online questionnaire survey completed by 32 faculty (53.3%), and interviews with 16 nursing faculty. Based on the findings, I concluded that online education is useful in these nursing programs when the content and the semesters/years are appropriate, and necessary supports are in place. Content containing complex cognitive concepts was perceived as better suited to face-to-face settings, as was experiential learning such as relational practice and psychomotor skill mastery. A hybrid delivery format was the most preferred teaching environment. Faculty experienced challenges with developing higher level online discussions and having students collaborate. Faculty perceived that online teaching took much more time and there was a need for acknowledgement by leadership of this time in workload assignments. Though the colleges in this study are representative of the Ontario CAATs, the findings are not broadly generalizable. However, they will be of interest to other academic programs that wish to assess their own use of online learning, particularly in people and practice-based professional programs that prepare practitioners who work with vulnerable populations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".