Informal Learning Evidence in Online Communities of Mobile Device Enthusiasts
Bibliographic record
Abstract
This chapter describes a study that investigated the informal learning practices of enthusiastic mobile device owners. Informal learning is far more widespread than is often realized. Livingston (2000) pointed out that Canadian adults spend an average of fifteen hours per week on informal learning activities, more than they spend on formal learning activities. The motivation for these learning efforts generally comes from the individual, not from some outside force such as a school, university, or workplace. Therefore, in the absence of an externally imposed learning framework, informal learners will use whatever techniques,resources, and tools best suit their learning needs and personal preferences. As ownership of mobile technologies becomes increasingly widespread in the western world, it is likely that learners who have access to this technology will use it to support their informal learning efforts. This chapter presents the findings of a study into the various and innovative ways in which PDA and Smartphone users exploit mobile device functionality in their informal learning activities. The findings suggested that mobile device users deploy the mobile, connective, and collaborative capabilities of their devices in a variety of informal learning contexts, and in quite innovative ways. Trends emerged, such as the increasing importance of podcasting and audio and the use of built-in GPS, which may have implications for future studies. Informal learners identified learning activities that could be enhanced by the involvement of mobile technology, and developed methods and techniques that helped them achieve their learning goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".