Emergent, self-directed, and self-organized learning: Literacy, numeracy, and the iPod Touch
Bibliographic record
Abstract
This paper uses narrative and storying to retell how two girls ages 5 and 7 continue to make use of an iPod touch to expand their literacy and numeracy. The paper explores the stopwatch and alarm features within the clock application, as well as the weather application, and the Internet browsing capability of the iPod touch. It also explores some of the challenges and inequities inherent in using this type of technology. The paper concludes with the author’s belief that it is important to allow learners freedom to explore and freely play with the technology and that emergent, self-directed, and self-organized learning is a natural and gentle approach to lifelong learning. Becoming literate and numerate is infinitely complicated, yet very simple. It is infinitely complicated because there are so many variables within and without the individual who is learning these skills that understanding the process fully is nearly impossible. Yet, it is simple because so many successfully become literate and numerate so naturally. Relating to this point Schuerwegen (2011) writes, Ergo, when a child grows up in a reading, writing, counting environment, especially an electronically driven society such as ours, he will one day find the need to pick up all these skills, at least as much as he needs them. (p. 22)
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".