Older Adults’ Use of Online Personal Learning Networks to Construct Communities of Learning
Bibliographic record
Abstract
This study investigated how retired older adults (age 55+) use the Internet and social media tools to facilitate their informal, self-directed learning by creating and maintaining online personal learning networks (oPLNs). The research examined what information and communication technologies (ICT) participants included in their oPLNs and how they used these oPLNs to activate and self-direct their informal learning. Employing the web-conferencing tool WebEx, four online focus groups and four one-to-one audio interviews were conducted allowing for a total of 15 voluntary, geographically-dispersed participants from across Canada to synchronously interact and exchange their experiences and insights regarding their oPLNs. Using a thematic analysis method, the discussion transcripts generated were analyzed to examine learning contexts, strategies to manage learning, motivation to learn and achievement of learning goals, as well as to discover emergent themes. It was clear from our findings that oPLNs provided a virtual "learning community" that supported informal, self-directed learning via learner participation and interaction opportunities fostered by ICT-based tools and processes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".