Factors Influencing Teachers’ Use of Multimedia Enhanced Content in Secondary Schools in Tanzania
Bibliographic record
Abstract
Tanzania is faced with a severe shortage of qualified in-service school science and mathematics teachers. While science and mathematics account for 46% of the curriculum, only 28% of teachers are qualified to teach these subjects. In order to overcome this challenge, the Ministry of Education and Vocational Training (MoEVT) implemented a project to use multimedia-enhanced content to upgrade subject content knowledge of science and mathematics teachers in secondary schools. A total of 70 topics and 147 subtopics were developed and enhanced with various multimedia elements. The content was used to train 2,000 in-service science and mathematics teachers from secondary schools in 19 selected centers countrywide. However, the presence and availability of this content does not automatically guarantee that teachers will use them. For this content to improve teachers’ subject content knowledge, they must be accepted and used by teachers in secondary schools. This study examines factors affecting teachers’ acceptance and prolonged use of developed multimedia-enhanced content using the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as a research framework. A sample of 1,137 teachers out of 2,000 was collected and tested against the research model using regression analysis. With exception of performance expectancy, all other factors had a statistically significant effect on teachers’ acceptance and use of the developed content. The government and other stakeholders can use these findings to develop strategies that will promote acceptance and use of the developed content in secondary schools 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".