A Quasi-Experimental Study of a Web-Based English Literacy Tool for Grade 3 Students in China
Bibliographic record
Abstract
Purpose: This study explores the effectiveness of a A Balanced Reading Approach for Children Always Designed to Achieve Best Results for All (ABRACADABRA; hereinafter, ABRA)—a web-based literacy program designed by Concordia University in Canada—on third-grade students in Nanjing, China. Design/Approach/Methods: Participants comprised 999 students from three treatment schools (N = 711) and three control schools (N = 288). Three different approaches were used in the treatment schools: namely, a computer laboratory once a week, noontime study after lunch, and single-game instruction (SG) during every English lesson. Interviews were also conducted with teachers, producing qualitative data. Findings: Following 20 weeks of intervention, the overall effect size was +0.05. The SG group reflected the smallest effect size (d = -0.52). The noontime study group produced an effect size of 0.39, and the laboratory group an effect size of 0.55. This study conducted interviews with teachers to gain a qualitative understanding of the differential impacts. In doing so, this study found that teachers in the SG group were poorly motivated due to a lack of school support and heavy workload, resulting in passive roles and low ABRA program intensity. Originality/Value: The results of this study indicate that ABRA is an effective means of improving Chinese students’ English literacy skills. Results also underscore the need for critical measures to encourage teachers to actively participate in the program.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".