University Educators’ Experience of Personal Learning Networks to Enhance Their Professional Knowledge
Bibliographic record
Abstract
This paper explores the experiences of university educators who use personal learning networks (PLNs) to enhance professional knowledge. With growing expectations to design and deliver effective online learning experiences, the PLN may offer flexible and supportive professional learning opportunities that build digital pedagogical capabilities. Previous research investigating PLNs has focused on how school teachers leverage social technologies to build these networks. However, there is limited examination of PLN use by university educators. This research is informed by the theories of networked learning and connectivism and uses a case study approach to deeply consider the experiences of five university educators from different disciplines across the globe. They share their understanding of the concept of the PLN, the influence of the COVID-19 pandemic, and how their PLN affects their digital pedagogies. The findings reveal nuanced insights of university educators’ real-life experience, shedding light on how the use of social media and other digital tools for professional learning is changing and the implications this has for the development of university educators’ understandings of digital pedagogies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".