Bibliographic record
Abstract
Background and objective: Online instruction is very different from teaching in a face-to-face setting and educators may lack formal pedagogical training specific to online instruction; in addition, online instructors may feel isolated and have less access to direct support than their counterparts on campus. The objective of this study was to promote best practice in online education through faculty support and professional development; a structured online training process was created.Methods: Design: Instructors that teach in the online venue need teaching and training to feel comfortable with the technology and online pedagogy strategies that support best practice in online education. A structured training process was created to support novice online educators. Setting: Nursing faculty and Masters of Science in Nursing education track students co-taught one online class together. Participants: Faculty and senior level Masters of Science in Nursing education track students were asked to reflect on their one-year teaching and training experience as educators. Methods: Qualitative analysis using Denzin’s interpretive interactionism was used to elicit meaning from participant experiences.Results: Four themes emerged from the data; online pedagogy, knowledge acquisition, mentor-mentee role, and online nurse educator. These themes align with the scholarship of teaching, discovery, application, and integration, respectively. The Training Model for Online Nurse Educators was developed to show this relationship.Conclusions: Using Boyer’s model of scholarship as a framework for online training can prepare instructors for the online nurse educator role. Online instructional delivery is a mainstay in education necessitating nurse educators who are prepared to apply best practice strategies in online education.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".