Using a Touch-Based, Computer-Assisted Learning System to Promote Literacy and Math Skills for Low-Income Preschoolers
Bibliographic record
Abstract
The use of touch-based technologies by young children to improve academic skills has seen growth outpacing empirical evidence of its effectiveness. Due to the educational challenges low-income children face, the stakes for providing instructional technology with demonstrated efficacy are high. The current work presents an empirical study of the use of a touch-based, computer-assisted learning system by low-income preschoolers. A description of the system’s design is provided with attention to young children’s interaction with touch devices, learner engagement, and pedagogically-based delivery of academic content. Children in 18 low-income child-care preschool classrooms were assessed on literacy and math skills in the fall and again in the spring. Target children used the iStartSmart learning system throughout the academic year, while control children did not have access to the system. Compared to controls, children using the learning system made significant gains on external standardized measures of literacy and math. Children who spent more time using the system and those who reached the upper levels of skill understanding showed the strongest improvement in test scores. The findings contribute to the currently sparse literature by illuminating that for at-risk early learners, touch-based, computer-assisted instructional technology shows promise as an educational tool.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".