Using computer-based instruction to improve Indigenous early literacy in Northern Australia: A quasi-experimental study
Bibliographic record
Abstract
<span>The effectiveness of a web-based reading support tool, ABRACADABRA, to improve the literacy outcomes of Indigenous and non-Indigenous students was evaluated over one semester in several Northern Territory primary schools in 2009. ABRACADABRA is intended as a support for teachers in the early years of schooling, giving them a friendly, game and evidence-based tool to reinforce their literacy instruction. The classroom implementation of ABRACADABRA by briefly trained and intensively supported teachers was evaluated using a quasi-experimental pretest, post-test control group design with 118 children in the intervention and 48 in the control. Children received either a minimum of 20 hours of technology-based intervention or regular classroom teaching. Results revealed both Indigenous and non-Indigenous students who received ABRACADABRA instruction had significantly higher phonological awareness scores than their control group peers. The effect size for this difference was large (eta squared=.14). This finding remained when controlling for student attendance and the quality of general non-technology-based literacy instruction. Limitations of the study and implications for effective practice in remote and regional contexts are discussed.</span>
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".