Perceived Community Impact of International Experiential Learning
Bibliographic record
Abstract
Introduction: Kolb’s experiential learning model suggests that learning is enhanced through concrete experience, reflection, abstract conceptualization, and active experimentation (Slavich & Zimbardo, 2012, p. 573). Although the benefits of Kolb's experiential learning theory on the learner have been well documented, there is a gap in the literature on the impact of these opportunities on the local community and organizations. This study, realizing the benefits of Kolb’s theory, investigates the potential intended and unintended impacts on local communities/organizations by looking at how participants in international community-based learning or volunteer trips perceive their impact on local communities. The primary objective is to explore how Queen’s University’s HSCI 595: Cross-Cultural Determinants of Health course students perceive Tanzanian organizations’ and communities’ views on international learning trips like HSCI 595. The secondary objective is to compare these insights with perspectives from individuals who have participated in other international volunteering and experiential learning experiences. Methods: To achieve this, the study uses daily transformative journaling during the HSCI 595 Tanzania trip and survey questions as primary data collection methods. Additionally, it will conduct a literature review to assess the findings of any previous research done on this topic. The qualitative data will be analyzed through thematic analysis, identifying recurring patterns and key themes that emerge from participants' reflections and survey responses. Implications: By addressing a serious gap in literature, this research aims to gain insight into the perceived impacts of experiential learning and volunteering on local communities/organizations. The findings will contribute to a broader understanding of international community-based learning. By comparing experiential learning trips with more traditional volunteer work, this study provides new insights into how international experiences influence both participants and the communities they seek to support.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".