Bibliographic record
Abstract
This paper provides a review of the literature from 2009 to 2014 on student use of technology in early childhood education. Previous efforts to synthesize the literature are somewhat dated, non-specific about age range, and focus almost exclusively on literacy. Thirty peer-reviewed articles from 11 countries, selected from a comprehensive search of the literature, were organized under five main categories: literacy, engagement, social interactions, mathematics, and miscellaneous topics. The overall effect size, based on only 12 studies and 33 measures was moderately high (d= 0.71, SD=0.60). Considerable qualitative and quantitative evidence indicated that technology had a significant impact on literacy development. Fewer studies, mostly qualitative in design and small in sample size, reported that technology had a positive impact on engagement, social interactions, and mathematics skills. A handful of studies provided qualitative evidence that technology had a positive impact on sequencing, visual perception, creative thinking, and fine motor capability. Methodological concerns included limited sample sizes and descriptions, not documenting the consistency and accuracy data of collection tools, the extent of adult intervention, and the limited range of technology tools used.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".