The Challenges of Using Technology in Vocational Education and Their Impact on Students' Achievement from the Teachers' Point of View in Ramtha District Schools in Jordan
Bibliographic record
Abstract
The current article aimed to investigate the challenges of using technology in vocational education (VE). It investigated their effects on the achievement of students from the teachers' perspective in the schools in Ramtha. A sample consisting from (77) VE teachers in Ramtha, Jordan was chosen through the random method in sampling. This work used a survey. The survey that was used in this work consists of two main. The first part aims to collect personal data about the sample (i.e. gender and academic qualification). As for second part, it aims to collect data about the challenges of using technology in vocational education from the view of the sample. The researcher concluded that the severity of the challenges related to technology in teaching vocational education is moderate from the view of teachers. It was found that the most serious challenges related to the use of technology are represented mainly in the challenges related to technological applications, challenges related to school capabilities, and challenges related to curricula. It was found that there are challenges that significantly affect the students' achievements from the view of VE teachers. The researcher of the present study recommends developing the infrastructure in public schools in order to enable vocational education teachers to use technology in the teaching and training processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 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".