Effects of online leisure education on leisure behaviors and experiences among university students
Bibliographic record
Abstract
Leisure education (LE) is designed to help foster positive leisure attitudes, identify leisure constraints and opportunities, and develop leisure skills and knowledge. Although LE is effective in many populations, its delivery has been predominantly in-person. We conducted an experimental study of a fully online LE intervention (ONLEI) with 96 university students. Our 8-week intervention occurred on a Moodle platform, featuring pre-module and post-module quizzes, information videos on YouTube, learning activities, online discussion forum, and private journaling. Our fidelity checks suggested intervention participants, on average, watched 63% of the videos, while also increasing their quiz scores. Rates for forum and journal engagement were moderate to low. Multilevel linear modeling indicated the ONLEI group showed statistically better trends for leisure participation, leisure satisfaction, and frustration of competence and relatedness needs in leisure, than the control group. However, the groups did not differ in terms of autonomy frustration and needs satisfaction in leisure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".