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Record W6907731387 · doi:10.25316/ir-19710

Cognitive influences of outdoor leisure trips on Vancouver Island University students

2024· dissertation· en· W6907731387 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2024
Typedissertation
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureTransformative learningStructural equation modelingRecreationTest (biology)CognitionLeisure activityPromotion (chess)

Abstract

fetched live from OpenAlex

Purpose: The purpose of this research is to examine and understand the psychological influence of outdoor leisure trips on VIU students. Rationale: Recruitment and retention of students is a challenge in Canadian postsecondary institutions such as VIU. Therefore, a challenge is to understand the factors that influence a student’s retention and dropout motivation. This study investigates the transformative effects of outdoor leisure trips on self-efficacy, emotional intelligence, across-cultural adaptability, and a sense of belongingness among Vancouver Island University students, proposing that engagement in such activities significantly enhances their psychological and social well-being. Methodology: A quantitative research design (post-positivist worldview) guides the framework of this study. A self-reported questionnaire served as the main tool for gathering information. Students from Vancouver Island University, including exchange students, were invited as participants. Structural equation modeling (SEM) was used to test the direct and indirect influence of psychological constructs (emotional intelligence- EI, self-efficacy-SE, cross-cultural adaptability-CCA, and a sense of belonging-SOB). Results: Statistical analysis confirmed that students participating in outdoor leisure trips have higher SE, EI, CCA, and SOB (M ≥ 3.5). Independent samples t-test confirmed statistically significant differences among students who have exposure to academic/nonacademic leisure trips than those who do not, along with Cohen’s d varying between 0.5 and 0.7 validating that results are not only statistically significant but practically significant. SEM results proved a direct and positive relationship between emotional intelligence (β =.456, t = 2.32, p= .02) and cross-cultural adaptability (β = .577, t = 5.10, p < .001) on the sense of belonging while self-efficacy (β = .87, t = 2.76, p = .006) indirectly influenced SOB by influencing cross-cultural adaptability. The squared multiple correlation was 85% (R2 = .85) for the sense of belonging. The model fit explained 85 percent of the variation in SOB through SE, EI, and CCA. The fit indices: CMIN/df= 2.84, GFI= .95, TLI= .94, CFI= .96, SRMR= 0.45, and RMSEA= .07 were within acceptable range for the model fit. Implications: The results of this study may guide in framing policies by an academic institution while budgeting recreation and leisure facilities for students as students’ academic and social performance was found statistically as well as practically (effect size) significant. Understanding ways to achieve a sense of belonging can help universities build a competitive advantage for recruiting prospective students as well. On a societal front, this research shows the social sustainability aspect among students of different cultures, as cross-cultural adaptability was found to be significantly different for students participating in OLTs. In a broader field, this study enriches educational psychology by scientifically testing cognitive development and the impact of experiential learning on post-secondary students.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.011
GPT teacher head0.256
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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