Nursing Students Desire to Belong in Online Learning Environments. Part 1: A Mixed-Methods Study
Bibliographic record
Abstract
Background: In early 2020, due to the declaration of the COVID-19 pandemic, many educational institutions significantly modified their learning environments from in-person classrooms to online delivery to ensure the continuity of education and limit the spread of the virus. Understanding how undergraduate nursing students experience belonging during this drastic shift to online learning is an area of inquiry that has a limited amount of research. Purpose of the Study: The goal of this study was to describe nursing students’ sense of belonging in an online learning environment as well as identify strategies and supports to foster their sense of belonging. Methods: An explanatory sequential mixed-methods design with two phases was used for this study. In phase 1, the focus of this paper, we used a mixed-methods approach. The quantitative component consisted of an online survey and the qualitative component was the three open-ended questions included as part of the survey. Participants were third-year nursing students from a collaborative nursing degree program who had recently transitioned from the college to the university site and were currently taking all courses online as a result of the COVID-19 pandemic. Results: This paper reports on phase 1 of the study conducted in April 2021. The survey data were analyzed using descriptive statistics and the three open-ended questions were analyzed using thematic analysis. Conclusions: As more educational institutions continue with online courses during the pandemic and post-pandemic, the findings from this study will provide valuable information as to the factors that foster or hinder nursing students’ sense of belonging in an online learning environment. These findings will also assist in contributing to the development of creative teaching modalities and pedagogies which can help to enhance the quality of student learning experiences and their sense of belonging in online learning environments.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".