Exploring nursing student and faculty perceptions of incivility in the online learning environment
Bibliographic record
Abstract
Objective : The purpose of the study was to explore student and faculty perspectives regarding what constitutes incivility in the online learning environment (OLE). Online learning is increasingly prevalent in nursing education. Scant research describes characteristics of OLEs contributing to, or inhibiting, learning or affecting civility among learners/faculty. Educators must effectively manage uncivil behavior to reduce incivility in learning and work environments. Attribution theory is helpful in examining issues related to incivility. Methods and Results : Faculty (n = 34) and students (n = 44) reported perceptions of what constitutes incivility via an online survey, the Incivility in the Online Learning Environment (IOLE), an instrument described by Clark and colleagues. The groups reported somewhat different perceptions of the extent of incivility experienced but agreed on identification of uncivil behaviors. Both groups agreed that rude comments and name calling were definitely uncivil, however, several areas of disagreement existed between faculty and student groups as to other types of behaviors. For example, lack of timely feedback on assignments and an unclear syllabus were seen as incivility by students. High internal consistency (Cronbach’s alpha of .98 on the faculty scale, and .96 on the student scale) was obtained for the IOLE in this sample. Qualitative comments regarding suggested ways to promote civility were similar from both groups, including role modeling and penalizing incivility; students emphasize the need for clearly stated course requirements. Conclusions : Best practices are essential for developing/delivering online courses and in orienting faculty and students regarding expectations for professional behavior online. Faculty development should focus on using best practices to ensure online courses incorporate essential components enabling accessibility and efficiency for students navigating through them. Attention to this unique and increasingly typical learning environment is essential to the goal of prevention of incivility in both learning environments and subsequently in workplaces.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".