Group Writing, Reflection, and Discovery: A Model for Enhancing Learning on Wilderness Educational Expeditions
Bibliographic record
Abstract
Background: Understanding strategies for enhancing learning is central to developing effective teaching practices. Students’ perceptions of these practices are critical for deepening this understanding. Purpose: The aim of this study was to investigate students’ perceptions of a group journal activity (GJA) on learning enhancement and to present a model that illustrates the reflective-learning process of the GJA. Methodology/Approach: Three questionnaires (pre, post, and 2 months after) were distributed to 22 expedition participants in 2013 and 2015. Responses were analyzed to identify the learning benefits of the GJA, and grounded theory was used to develop the process model. Findings/Conclusions: A positive relationship exists between students’ participating in the GJA and self-reported perception of learning. Students report the GJA as having similar benefits as traditional personal journal-writing with the added benefits of promoting learning from others’ perspectives, enhancing understanding of others, promoting reflective communication skills, and providing a tangible record of experience which enables continued reflection and learning. A process model illustrates the recursive cycle of writing, sharing, and discovery that the GJA enables. Implications: This research presents the GJA as an uncommon form of journaling in outdoor education and demonstrates the GJA’s potential for enhancing learning. The model outlines the effective use of the GJA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".