Statistical Prediction and Marketing Recommendation of Foreign International Students’ Consumer Behavior
Bibliographic record
Abstract
Under a dynamic and complex educational market, it forces shape the educational environment. In the context of China, accelerating economic growth produces multiple newly Chinese multinational education institutions, which lack accurate analysis of consumer preference’s inherent characteristics with educational needs. Therefore, this research is vital in helping new Chinese multinational education institutions make decisions based on foreign countries’ students’ consumer preference and further filling the Chinese multinational education institution preference analysis’ gap. In statistics, this paper uses the Data collected from OECD/UIS/Eurostat (2021) Table B6.1, throughout 45 countries, ranging including bachelor's degree, master and doctorates foreign countries students studying in China, to conduct regression analysis intensely observing foreign international students’ Country of Attendance preference. In Marketing, Multi-factor integration model authenticates the overall international student's consumer performance. It is proved that Chinese educational institutions’ attraction is dominantly attributed to stable economic growth, advanced information, and communication technology. Specifically, China has a higher affinity towards OECD country students for courses of tertiary, bachelor, master, and doctoral studies. Foreign international students' preference statistics prediction improved the accuracy of foreign international students’ behaviors towards the Chinese educational area, driving Chinese educational institutions to a more precise and effective marketing strategy. These results shed light on foreign international students' preference for Chinese education, and how should educational institutions change their marketing methods next.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".