Scaling up a Technology-Based Literacy Innovation: Evolution of the Teacher Professional Development Course
Bibliographic record
Abstract
Good teachers are a major predictor of students’ success in school and beyond it. Finding ways to increase the quality of teaching has been a concern for educational systems across various income contexts and, particularly, in the Global South. This paper discusses the iterative design of an online teacher professional development program geared to improving teachers’ English language instruction by means of implementing early literacy software. The program was implemented in various modes (face-to-face, blended and online) with early primary teachers scattered throughout Kenya during the pandemic school closures and after reopening. Relying on the blended learning approach, a potentially effective technology-driven TPD offers multifaceted content, has adaptive and flexible design, and is ongoing until mastery of core concepts is achieved. Further, such solution develops motivational dispositions of teachers about teaching with early literacy software so that its perceived value and the likelihood of success are high, and the benefits outweigh the costs of implementation. The next step of this research is to learn about the specific outcomes of the blended TPD, including changes in literacy instruction and subsequent improvements in student literacy skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".