Challenges and instructors’ intention to adopt and use open educational resources in higher education in Tanzania
Bibliographic record
Abstract
Higher education in Tanzania like in many other Sub-Saharan countries suffers from unavailability of quality teaching and learning resources due to lack of tradition, competence, and experience to develop such resources. Nevertheless, there are thousands of open educational resources (OER) freely available in the public domain that can potentially improve the quality of existing resources or help to develop new courses. The uptake and reuse of these resources in higher learning institutions (HLIs) in Tanzania has been very low. The study applied the unified theory of acceptance and use of technology (UTAUT) model to elicit instructors’ intention to adopt and use OER in teaching. The paper also investigated challenges that hinder instructors to adopt and use OER. A sample of 104 instructors selected randomly from five HLIs was collected and tested against the research model using regression analysis. The study found effort expectancy had significant positive effect on instructors’ intention to use OER while performance expectancy, facilitating conditions, and social influence did not have significant effect. Challenges that were found to hinder instructors to adopt and use OER are discussed. The findings of this study will help those who are involved in OER implementation to find strategies that will maximize OER adoption and usage in higher education in Tanzania.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".