Learning to <scp>eRead</scp>: A qualitative exploration of young children's developing <scp>eReader</scp> practices
Bibliographic record
Abstract
Abstract Background Previous eBook studies were primarily cross‐sectional surveys or experimental studies providing a snapshot of the impact of eBook reading on children's emergent literacy skills. Scholars have yet to characterize more fine grain developmental progressions in the use of eReaders and similar devices – that is, how children's eBook‐related practices evolve over time and what might be changing behaviourally over the course of repeated readings for any age group. Objectives This is the first study to characterize young children's (2 to 3 years) use of eReaders over the span of several months under semi‐naturalistic conditions. Methods Twenty five mothers and their 2.5‐ to 3.5‐year‐old child engaged in a series of documentation exercises and interviews around use of a novel eReader device. This study is exploratory and deductive in nature, and hypotheses were not set forth a priori. Results and Conclusions The results of the present study suggest that children develop increasing mastery of the affordances of eReaders as their device exposure increases. Over time, their ability to independently engage with devices rapidly increases. They transition from needing technical support from parents and high levels of encouragement to attend to literacy content over hotspots, to sometimes using the device independently, to considering the device a mainstay in their home – no longer a novelty. Implications for practice and/or policy The developmental usability patterns noted in this study extend our naturalistic understanding of young children's dexterity with digital devices and point to methodological approaches that might yield more ecologically valid findings in future studies.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".