“We dream of climbing the ladder; to get there, we have to do our job better”: Designing for Teacher Aspirations in rural Côte d’Ivoire
Bibliographic record
Abstract
As governments in developing countries race to solve the global learning crisis, a key focus is on novel teaching approaches as taught in pedagogical programs. To scale, these pedagogical programs rely on government teacher training infrastructure. However, these programs face challenges in rural parts of Africa where there is a lack of advisor support, teachers are isolated and technology infrastructure is still emerging. Conversational agents have addressed some of these challenges by scaling expert knowledge and providing personalized interactions, but it is unclear how this work can translate to rural African contexts. To explore the use of such technology in this design space, we conducted two related studies. The first was a qualitative study with 20 teachers and ministry officials in rural Côte d’Ivoire to understand opportunities and challenges in technology use for these stakeholders. Second, we shared a conversational agent probe over WhatsApp to 38 teachers for 14-weeks to better understand what we learned in the survey and to uncover realistic use cases from these stakeholders. Our findings were examined through a theoretical lens of aspirations to discover sustainable design directions for conversational agents to support teachers in low infrastructure settings.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".