Understanding the Decision-Making Process of College-Bound International Students: A Case Study of Greater Toronto Area Colleges of Applied Arts and Technology
Bibliographic record
Abstract
This qualitative study examined the process whereby international college students from various countries choose their country of study, type of institution, specific college, and program. It identified and explored the relative importance of each decision-making factor. Fifty-five international students attending four Greater Toronto Area (GTA) colleges of applied arts and technology (CAATs) took part in the study. A modified version of the decision-making model was used to outline the different stages of the participants’ decision-making process. The theoretical framework used for the study is based on three theories: push and pull factors; human capital; and status attainment. The findings revealed that international college students rely heavily on recruitment agents, friends, and relatives in their decision-making process. The main reason they chose Canada is its student-friendly immigration policy. The most important factors for choosing college over university are low tuition fees and the practical nature of a college education. The study proposes an enhanced model of international students’ decision-making process as it applies to Ontario CAATs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".