Why Do Saudi High Schools’ Graduates Enroll in the Colleges of Technology: A Case Study?
Bibliographic record
Abstract
<p class="apa">The paper aims at elaborating a strategy regarding students’ admission at the colleges of technology in the western region of Saudi Arabia. Purposely, the study surveys the opinion of the students attending the Jeddah College of Technology (J.C.T) and reports on seven socio-economic factors, specifically, the <em>J.C.T. evaluation system</em>, the <em>high school G.P.A</em>, the <em>training period, </em>the<em> trainee “gut feel”</em>, the <em>scholarship</em>, the <em>professional project at graduation</em> and the <em>family financial support. </em>A proportionate stratified random sample of 340 students has been drawn and asked to rate the influence of each factor upon their decision making prior enrolling the J.C.T. The sample has managed students who emanate from 11 sections (sub-strata) and four independent Depts., <em>Management</em> &amp; <em>Tourism</em>, <em>Mechanical Technology</em>, <em>Electrical Technology</em> and <em>Civil</em> &amp; <em>Architecture</em>. As we seek to ascertain different factors weights on the students’ decision making, the study has considered three major pooling schemes (strata), i) <em>as per</em> Depts., ii) <em>as per</em> seniority at the J.C.T, iii) and, <em>as per</em> the living location. For the pooling methods, one another, we investigated the correlation between the students’ decision making to enroll at the J.C.T.<em> </em>and each one of the study factors as cited beforehand. The Chi-square test is used to assess such a categorical association. Finally, a formal method was devised to determine the factors prevalence based on the Chi-square significance level and the number of the pooling schemes wherein the test has proven significant at 5% of significance level. The research findings have showed that the “high school G.P.A” and the “J.C.T evaluation system” factors are the most influential, orderly.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".