Flipped Classroom with Virtual Reality Technology Learning Model for Chinese College Students in Psychological Education: A Need Assessment Study
Bibliographic record
Abstract
The growing prevalence of psychological health problems, particularly depression and anxiety, among Chinese college students necessitate innovative approaches to psychological education. This research investigates the psychological education needs of college students and proposes a flipped classroom model integrated with virtual reality (VR) technology to address these needs. A needs assessment survey, employing a 16-item questionnaire with a five-point Likert scale, was conducted to collect data from 400 students selected by stratigied eamdom sampling method from 125 higher education institutions in Anhui Province. The survey explored student perspectives on textbook content, learning outcomes learning methods and technologies. Subsequently, a flipped classroom model incorporating VR technology was developed based on a review of existing literature. This model was then evaluated by a panel of five experts across dimensions of suitability, model components, and learning activities. Quantitative data from the student needs assessment and the expert evaluation were analysed using descriptive statistics. Results from the needs assessment indicated a strong consensus among students (mean scores 4.12-4.77) regarding the importance of psychological education, with 15 of 16 items receiving "strongly agree" ratings. The proposed flipped classroom model, comprising before-class, in-class, after-class and evaluation components, received high ratings from the expert panel (mean scores 4.6-5.0) across all evaluation dimensions. The experts "strongly agree" with the model's suitability and potential effectiveness. This study offers an innovative pedagogical framework for psychological health education in higher education, leveraging VR technology within a flipped classroom design to support students in managing anxiety and depression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".