Bibliographic record
Abstract
Adventure learning (AL) is an approach for the design of digitally-enhanced teaching and learning environments driven by a framework of guidelines grounded on experiential and inquiry-based education. The purpose of this paper is to review the adventure learning literature and to describe the status quo of the practice by identifying the current knowledge, misconceptions, and future opportunities in adventure learning. Specifically, the authors present an integrative analysis of the adventure learning literature, identify knowledge gaps, present future research directions, and discuss research methods and approaches that may improve the AL approach. The authors engaged in a systematic search strategy to identify adventure learning studies then applied a set of criteria to decide whether to include or exclude each study. Results from the systematic review were combined, analyzed, and critiqued inductively using the constant comparative method and weaved together using the qualitative metasynthesis approach. Results indicate the appeal and promise of the adventure learning approach. Nevertheless, the authors recommend further investigation of the approach. Along with studies that investigate learning outcomes, aspects of the AL approach that are engaging, and the nature of expert-learner collaboration, future adventure learning projects that focus on higher education and are (a) small and (b) diverse, can yield significant knowledge into adventure learning. Research and design in this area will benefit by taking an activity theory and design-based research perspective.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".