Understanding the Longitudinal Impact of a Chatbot to Facilitate a Virtual Community of Practice for Teachers in Rural Côte d’Ivoire
Bibliographic record
Abstract
Communities of practice can improve teachers’ professional development through informal in-person discussions among community members. However, infrastructural challenges pose difficulties in fostering in-person connections, particularly in rural communities in the Global South. The emergence of social media and chatbots has presented an avenue for creating virtual communities for teachers, especially those in rural areas. An unanswered question is the potential impact of a chatbot-supported virtual teacher community on teachers’ professional development. To answer this question, we conducted a longitudinal quasi-experiment involving 313 teachers participating in a new training program in rural Côte d’Ivoire by deploying a chatbot on Facebook Messenger. Our experiment had two chatbot versions for two regions, i.e., one version supporting virtual community and one control. Our findings indicate that teachers in the virtual community condition exhibited modest enhancements in motivation and knowledge indicators. We make a case for implementing virtual communities of practice facilitated by chatbots to bolster the professional development of teachers in rural African contexts.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".