Exploring Educational Technology Policies and Practices of the World Bank
Bibliographic record
Abstract
With information and communications technologies proliferating into all aspects of life during the COVID-19 crisis, education has experienced “the world’s biggest educational technology (EdTech) experiment in history.” While the effectiveness of educational technology in increasing learning and decreasing inequality is still contested, international organizations such as the World Bank, have become a driving force in promoting ICT use in education. Yet there is not much research on how the World Bank supports ICT use in education conceptually and financially.This inquiry uses a case study approach to look at the World Bank’s policy prescriptions and funding of ICT in education between 2011-2022. It does this through a document analysis of World Bank research, formal policy documents, and its portfolio of projects in K-12 education in Sub-Saharan Africa (SSA). The analysis first describes the evolution of the Bank’s EdTech policies over time. It then examines the changes in education sector projects with ICT components in SSA over the same period. Finally, the study compares how those policies and projects align. Findings from this study suggest that the World Bank’s EdTech policies shifted from system-level prescriptions regarding infrastructure, management, monitoring, and evaluation pre-pandemic, to more instructional approaches during the pandemic. Similarly, the Bank’s pre-pandemic education projects with ICT in SSA showed more inclination towards system management, specifically in low-income countries. In contrast, during the pandemic, the Bank’s EdTech financing focused on curriculum, pedagogy, and equity, and advocated “multimodality” – the use of as many available ICT tools as possible for remote education. The comparison of the Bank’s EdTech policies and its financed projects with ICT components in SSA suggests that the Bank’s response to the pandemic has led to greater alignment between its EdTech policy advice and investments compared to previous periods. However, as per the Bank’s plan to continue its promotion of blended learning, more research on the Bank’s EdTech approach is needed to track how its support of EdTech affects its borrowing countries, as well as how its work compares to that of similar international organizations (such as UNESCO or OECD) and other actors in the EdTech field (such as civil society or technology and telecommunications firms).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".