A comparative study on technical education curriculum at middle school (Iran and four other countries)
Bibliographic record
Abstract
In this study, we compared Iran’s career and technical education curriculum with similar curricula in two developed countries (America & Canada) and two developing countries (Pakistan, Morocco), using Beredy’s method. Our aim was using the experiences of other countries for promoting Iran’s career and technical education curriculum. Results of the comparison indicated that the above mentioned countries have included topics of Iran’s career and technical education in more than one subject matter. Information technology education and giving the right of selection to students were common features of the curriculum of these countries. In the two mentioned developed countries, the subject matters related to career and technical education is instructed based on the need to lifelong learning skill and technology literacy. Curriculum goals are using and evaluating technologies. The pedagogy component of these curricula focus on technology process (Designing & problem solving), and not on content, so theory assessment is integrated with practical assessment. In the two mentioned developing countries, the related courses for middle level school (6-9) are planned according to national needs and curricula implementation capability. Getting knowledge and skill in information technology, home economy and business affairs are common objectives and content in these countries’ curricula. Also, they focus on active teaching methods and student’s assessment is both practical and theoretical. Findings suggested that we could determine a name and rational for career and technical education curriculum based on modern life and work needs. In order to instruct technology literacy and necessary skills of life, it is better the curriculum be divided into two optional parts as “technology education” and “life skill education”. Also, these curricula could focus on instruction of designing and problem solving.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".