Developing Institutional Open Educational Resource Repositories in Vietnam: Opportunities and Challenges
Bibliographic record
Abstract
The introduction of open educational resources (OER) provides new opportunities for learners worldwide to access high-quality educational materials at the lowest cost. As a developing country, Vietnam is one of the countries that can most benefit from the OER movement. However, the concept of OER in Vietnam remains little known to the public, with few institutional OER repositories (IOER) developed. This study contends that IOER development in Vietnam is complicated and constrained by many contextual difficulties; it was designed to explore the challenges and opportunities. After a literature review, 20 semi-structured interviews were conducted with relevant stakeholders. Building on the findings from the literature, this study found that IOER development in Vietnam is constrained by five categories of challenges: (a) technological and infrastructure matters, (b) economic constraints, (c) sociocultural characteristics, (d) pedagogical concerns, and (e) legal limitations. Many of these challenges are not identified in the literature and provide insights into potential implications and solutions for future IOER in Vietnam and other countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".