Investigating perceived barriers to the use of open educational resources in higher education in Tanzania
Bibliographic record
Abstract
The past few years have seen increasingly rapid development and use of open educational resources (OER) in higher education institutions (HEIs) in developing countries. These resources are believed to be able to widen access, reduce the costs, and improve the quality of education. However, there exist several challenges that hinder the adoption and use of these resources. The majority of challenges mentioned in the literature do not have empirically grounded evidence and they assume Sub-Saharan countries face similar challenges. Nonetheless, despite commonalities that exist amongst these countries, there also exists considerable diversity, and they face different challenges. Accordingly, this study investigated the perceived barriers to the use of OER in 11 HEIs in Tanzania. The empirical data was generated through semi-structured interviews with a random sample of 92 instructors as well as a review of important documents. Findings revealed that lack of access to computers and the Internet, low Internet bandwidth, absence of policies, and lack of skills to create and/or use OER are the main barriers to the use of OER in HEIs in Tanzania. Contrary to findings elsewhere in Africa, the study revealed that lack of trust in others’ resources, lack of interest in creating and/or using OER, and lack of time to find suitable materials were not considered to be barriers. These findings provide a new understanding of the barriers to the use of OER in HEIs and should therefore assist those who are involved in OER implementation to find mitigating strategies that will maximize their usage.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".