“Journal Writing is Something We Have to Learn on Our Own” — The Results of A Focus Group Discussion With Recreation Students
Bibliographic record
Abstract
Reflective journals have become an increasingly popular tool used by numerous instructors in many academic fields, including recreation and leisure studies. Previous research and narrative reports of journal writing have indicated there are several positive and negative aspects of journal writing for students. However, many aspects of journal writing remain poorly understood. In this paper, we describe the results of a focus group discussion centered on journal writing held with nine students who were enrolled in a post-secondary recreation program. By and large, the students who participated in this focus group enjoyed and valued their journaling experiences. They were, however, cautious about certain aspects of the journaling process and offered numerous suggestions for improving the ‘journaling experience.’ Five themes were explored in this focus group, including: 1) general journaling behaviors, 2) barriers to journaling, 3) evaluation of journals, 4) gender differences in journaling, as well as 5) self-perceptions of journaling. This paper concludes with 10 recommendations to be considered by recreation and leisure studies instructors who use journaling as an instructional technique.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".