Preliminary investigations for the development of a virtual reality-based English-language communication program: Using the Delphi method
Bibliographic record
Abstract
The purpose of this study was to gather opinions from experts via the Delphi method to inform the future development of a virtual reality based English language communication program for university level students in Korea. The participants, who consisted of a panel of experts and professors who majored in English language and multimedia education, completed three Delphi surveys based on Context, Input, Process, and Product evaluation, which is referred to as CIPP. In the first Delphi survey, the participants answered multiple choice questions and open-ended questions related to four areas relevant to the development of a virtual reality based program. Based on their answers, a second Delphi survey was designed to determine the participants' level of agreement with the appropriateness of the questions related to the four areas. In the third Delphi survey, participants were shown the results (mean, standard deviation, median, interquartile range, consensus chart, and convergence degree) and were asked to confirm or modify their answers based on the other participants' answers. According to the analysis of the Delphi survey results, need for the development of a virtual reality based English language communication program was suggested, and recommendations were made regarding the content and application of the program.
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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 | 0.075 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".