Teaching multilingual literacy in Ugandan classrooms: The promise of the African Storybook
Bibliographic record
Abstract
Abstract For over a decade, the authors have worked collaboratively to better understand and address the challenges and possibilities of promoting multilingual literacy in Uganda, a country of over 44 million people where over 40 African languages are spoken and English is the official language. This article focuses on the diverse ways that teachers promote early literacy in large multilingual classrooms, and how the innovative African Storybook digital initiative might support primary school teachers in both rural and urban areas. We begin the article with a description of our collaborative work on the African Storybook ( http://www.africanstorybook.org/ ) and one of its derivatives, Storybooks Uganda ( https://global-asp.github.io/storybooks-uganda/ ). Then, drawing on a collaborative study of primary school classrooms in eastern Uganda, we analyze four common strategies that Ugandan teachers use to promote multilingual literacy in their classrooms: the use of the mother tongue as a resource; songs and multimodality; translanguaging; and linguistic strategies for classroom management. We follow this with a discussion of a 2015 teacher education workshop in eastern Uganda, which illustrates how the African Storybook can help support Ugandan teachers as they navigate the challenges of large classrooms. We conclude that the African Storybook has much promise for addressing the United Nations’ 2030 Sustainable Development Goals.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".