Social Media in Educational Practice: A Case Study of an Ontario School of Nursing
Bibliographic record
Abstract
Social media can provide a tool for nursing students, who frequently transition between learning in the classroom and clinical contexts, to consolidate both their formal and informal learning experiences. Furthermore, the majority of baccalaureate nursing students fall within the millennial generation, meaning that they have grown up with computers and other digital tools and likely already use them to share educational resources and maintain contact with their peers. We know little about how health professions outside of Medicine use social media in teaching and learning, especially outside the context of the classroom and assignments. This pragmatic three-phase sequential mixed methods case study explores nursing students’ perceptions of using social media to support their learning and teaching. Phase 1 involves a survey of nursing students at Nipissing University to understand their use of social media for teaching and learning purposes. Phase 2 consists of a digital artifact collection, which involves following nursing students’ social media accounts to see what content they share related to teaching and learning in nursing education. Finally, Phase 3 involves semi-structured interviews to gain a deeper understanding of what motivates nursing students’ decisions to use social media for teaching and learning purposes. Overall, the findings show that nursing students at Nipissing University’s School of Nursing use social media in their formal and informal teaching and learning; they also use it as a ‘third space’ to supplement existing educational and institutional structures. The findings also demonstrate that while nursing students are relatively motivated to use social media in their teaching and learning, issues of quality and reliability of evidence, professionalism, and faculty or program attitudes can influence nursing students’ decisions to use or not to use social media for teaching and learning purposes. Finally, the findings suggest that nursing students share content related to advocacy, health education, and their perceptions and realities of nursing practice. This study contributes practically to the existing conversations regarding teaching and learning, critical inquiry, communication and collaboration, and professionalism in nursing education and practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".