Travelations: Travel and Learning
Bibliographic record
Abstract
Travel has been historically associated with learning and discovery because it broadens the perspectives of individuals, and they consequently learn from their experiences (Casella, 1997; LaTorre, 2011; Steves, 2009). The relationship between travel and learning has become an area of interest in recent years, as the pursuit of meaningful and memorable experiences becomes increasingly recognized as a central feature of tourism, and also as mainstream motivations for travel increasingly shift from hedonistic escapism to intellectual and cultural growth (Falk, Ballantyne, Packer & Benckendorff, 2011). Tourism organizations in Canada have responded to these currents and have identified a specific market segment of ‚Learning Tourists,‛ who are seeking to stimulate the mind and body and to be intellectually challenged through pleasure travel (Research Resolutions & Consulting Ltd, 2007). Multiple studies in the tourism and study abroad literatures attest to the learning benefits associated with being away from home (Byrnes, 2001; Gmelch, 1997; Hansel, 1998; Hunt, 2000; Kuh, 1995; Stitsworth, 1994). However, an understanding of the deeper reasons why travel promotes learning is lacking (Falk et al., 2011; Minnaert, 2013; Stone & Petrick, 2013; van’t Klooster et al., 2008). This thesis shares the outcomes of a mixed-methods study conducted to explore the relationship between travel and learning among emerging adults. Interviews were undertaken with a diverse group of 22 young adult travellers, hailing from a variety of different countries, and this data was supplemented with over 100 quantitative survey responses and with personal reflections of the author, in keeping with the overall methodological perspective of heuristic inquiry that guided the study. Taken together, the findings point to the importance of travel motivation, departure from one’s comfort zone, reflection, social interaction, and the building of one’s travel biography, all of which unfold over the course of the travel process and function to facilitate learning. The study draws on interdisciplinary literature from experiential education, psychology, and tourism studies to illuminate these issues, and then offers practical advice regarding how the insights derived might be useful for individual travellers, tourism businesses, and educational institutions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".