Determinants of College and University Choice for High-School Students in Qatar
Bibliographic record
Abstract
Drawing on existing research, this paper investigates various predictors of high school students’ college and university choice decisions in Qatar. Based on a 2015 survey of 1,427 participants, this study utilized exploratory factor analysis to identify variables that affect student choice of higher educational institutions (HEI). Three factors were extracted from the analysis, revealing the following aspects of the academic experience as important when choosing a HEI: quality of education, cultural values, and the cost of education. To further the understanding of the relevance of these factors for different student demographics, we employed ordinal logistic regression to test whether several independent variables (student’s gender, nationality, parental education, and parental occupation) act as significant predictors of the three extracted dimensions (dependent variables). The analysis revealed that, indeed, demographic characteristics significantly predict, to varying degrees, all three factors affecting student’s HEI choice. Discussion on postulated reasons behind the recorded relationships will follow, along with implications and recommendations for further study and research. Findings of this study will help HEIs in Qatar and the broader region to position themselves more effectively, and develop targeted strategies that attract a diverse student population.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".