Learning in the Wild: Exploring the Practice of Learning in Open, Online Forums
Bibliographic record
Abstract
The Internet provides many opportunities for learning from static resources to conversational spaces for questions, answers, commentary and exploration of topics of interest to participants, whether organized as Q&A sites such as Reddit, hashtag communities on Twitter, or knowledge-sharing sites such as Stack Overflow. Yet, there is limited research on how learning is happening in these spaces. This paper reviews literature and studies about learning in open, online forums to begin to synthesize what is known so far, and to set a research agenda addressing the question: How do people learn in open, online forums? The review builds on work by the author and colleagues, exploring what we refer to as ‘learning in the wild’ (in recognition of Hutchins’ “Cognition in the Wild”, and to reflect the ‘wilds’ of online forums such as Reddit). The increasing use and reach of these sites raises questions not only about what is being learned and what motivates participation in such sites, but also what kind of organization and learning practices are emerging. While it may be thought that such learning, taking place outside the bounds of institutional settings, is informal learning, the research suggests a more complicated picture, dependent on conversation, networks, membership in communities, and community practices, needing to be addressed by drawing on multiple disciplinary perspectives.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".