Exploring the Feasibility of Deploying Technology Enhanced School-Based Teacher Continuous Professional Development in Internet-Limited Environments in Tanzania
Bibliographic record
Abstract
In low-income countries, the use of technology to enhance teacher continuous professional development (TCPD) activities has been increasing significantly. However, most technology initiatives related to TCPD require the installation of complex information and communications technology (ICT) infrastructure in schools or availability of reliable Internet connectivity. While installation of ICT infrastructure is costly, the cost of the Internet is unaffordable to most teachers in low-income countries. This study explored the feasibility of deploying Raspberry Pi computers and tablets as micro-servers to facilitate school-based TCPD activities via a learning management system (LMS) without Internet connectivity. Teachers in eight schools in Dar es Salaam and Lindi accessed a TCPD sample module with Raspberry Pi and tablets providing hotspotting and treated as offline local servers hosting a LMS. After the trial, data was collected through focus group discussion, observation, and LMS logs involving 69 teachers. The findings showed that both Raspberry Pi and tablets could be used as micro-servers to provide access to learning resources in offline environments, but Raspberry Pi fared more favorably. Raspberry Pi was easy to set up and connected more devices than did the tablet. However, Raspberry Pi required careful handling as it is a delicate device. Interestingly, there was no significant difference in terms of the performance and cost of the two micro-servers. This study provided further evidence that both Raspberry Pi and tablets could be cost-effective approaches to deliver TCPD activities without installing complex ICT infrastructure or in areas with limited Internet connectivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".