Using the reading sciences and technology for teaching and learning in the Global South
Bibliographic record
Abstract
In the Global South, persistent literacy challenges have been exacerbated by schooling disruptions during the pandemic and afterwards. Addressing this problem requires teachers who both understand how to teach reading, and can implement it effectively and efficiently. This research examines the effects of an intervention combining a technology-based teacher professional development and implementation of new knowledge and skills and ABRA-READS interactive literacy software in early-primary classrooms in Kenya and Rwanda. Participants were 22 teachers and 1341 students from Kenya and 20 teachers and 1002 students from Rwanda. This quasi-experimental research featured the experimental teachers who implemented the intervention and their matching control teachers who taught reading in their usual way. Student reading outcomes were analyzed using hierarchical linear models (HLM). Teacher practices were assessed through self-reports, observations and trace data. Teachers shifted toward more student-centered instruction that incorporated decoding and comprehension, and students demonstrated significant reading improvements across gender and ability groups. Struggling readers in experimental classes made the largest gains, closing the gap with higher-reading peers in control classes. Findings demonstrate that blended TPD instruction, combined with ABRA-READS software, can positively change classroom practice and improve all students’ reading abilities. This intervention offers a promising a strategy to mitigate learning disadvantages early by offering students equal opportunities to succeed. While the global crisis in education, especially in LMICs, persists, this research suggests a solution. • Since the pandemic, more children in the Global South lack foundational lieracy skills. • These studies tested blended teacher training and implementation of literacy software in Kenya and Rwanda. • The intervention improved teacher practices, fostering student-centered, balanced literacy instruction. • All students benefited from the intervention, especially struggling readers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".