Path Analysis on the Factors Influencing Learning Outcome for Hospitality Interns–From the Flow Theory Perspective
Bibliographic record
Abstract
Learning outcome is an important indicator for educators in evaluating curriculum design. The focus of this study has been to examine the factors within internship programs, recognizing the complex nature of knowledge application in a practical industry environment. Flow theory was adopted to explain the psychological state of hospitality students during internship and relate it to their learning outcome. A total of 152 responses were collected via self-administrated questionnaires from hospitality students at their initial and final stages of internship in Taiwan. Results from SEM analysis indicate that both skill and the challenge from work have significant influence on the interns’ flow experience, wherein skill has a positive influence, while challenge does not. The flow theory was well confirmed at the final stage of the internship, which becomes the complete mediator for the skill and challenge to influence the learning outcome. Learning for the interns is not exclusively concerned with skill improvement, but includes a process to overcome the unfamiliarity of the challenge, which consequently leads to a direct positive effect on learning. Thus, proper challenge and improvement of skill are important counterparts, which influence the learning outcome simultaneously, where each of them cannot result in the proper learning outcome alone. The practical implication, which can be derived, is that proper cooperation between the educator and the intern supervisor should create an environment for optimum skill development, in which the challenge is balanced with the acquired new skills. Achieving such a balance via flow will facilitate a better learning outcome.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".