A Study on the Characteristics of Real Estate Education in Korean Universities through Text Mining-based Curriculum Analysis
Bibliographic record
Abstract
Text mining is a method of analyzing text data to derive the characteristics or status of a described object. Since an educational program's curriculum reflects the content and goals it aims to teach, text analysis of the curriculum can reveal the characteristics of the program. This study employs text mining techniques to analyze the curricula of real estate education programs at Korean universities and proposes strategies for future development. The paper presents three main findings: First, by analyzing the curricula of universities in the UK and US that offer real estate education, the study identifies the unique characteristics of each country’s approach, reaffirming previously identified differences between them. Second, real estate education at Korean universities features an eclectic curriculum that incorporates elements of both UK and US real estate education, resulting in diverse curricular offerings across institutions. Lastly, for individual Korean universities, the study provides guidelines for the development of their departmental education by indicating which UK or US universities have the most similar or different curricula.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".