Teachers’ Skills and ICT Integration in Technical and Vocational Education and Training TVET: A Case of Khartoum State-Sudan
Bibliographic record
Abstract
Information and communication technology (ICT) elicited rapidly dissemination over the world. For its impact inSudan, the national government has been energized the institutions to implement ICT in every sector. This studyexamined the Sudanese teachers’ skills and ICT integration in technical and vocational education and training TVETin Khartoum state. The study directed out of two hundred respondents were sampled randomly, questionnaires weredistributed, 168 (84%) were properly filled and returned, 130 were males and 38 females from three various bodiesof TVET include (technical secondary schools, artisan institutions, and vocational training centres). A One-WayANOVA and Independent sample t-test on SPSS version 20 for data analysis were adopted. The results revealed thatsignificantly the respondents are same in terms of demographic information and ICTs usage skills. However, therewas a significant difference among the respondents’ ICT skill due to their ages and qualifications. Consequently,more training needs to be conducted for TVET teachers in basic skills of computer use. Following the internationalstandards, the right decisions we are strongly recommending train/teach pre-service and in-service teachers on ICTsskills based on 21st-century requirement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".