The differences between students’ fixed and growth mindsets: a case of study tour between Hong Kong and Canada
Bibliographic record
Abstract
Purpose The paper aims to address the gap in the literature related to students’ mindsets and learning activities through investigation of the differences in students’ expectations of, feelings towards, and perceptions of an overseas study tour based on their mindset. The study provides an in-depth analysis of students with different mindsets and proposes the use of overseas tours and intercultural learning to foster students’ growth mindset. Design/methodology/approach An overseas study tour hosted by a self-financing tertiary institution in Hong Kong was selected for investigation. 13 sub-degree students participated in the study tour during the summer term in 2018. Two types of primary data – quantitative (i.e., a questionnaire survey) and qualitative (i.e., in-depth interviews) – of fixed mindset and growth mindset students were collected for analysis. Findings The findings indicate differences in students’ expectations of, feelings towards, and perceptions of an overseas study tour depending on whether they demonstrate a fixed or growth mindset. The growth mindset students had more and higher expectations of the study tour, all of which were related to personal growth and development. The fixed mindset students did not have as much of a desire for personal development and their expectations were easily met. Both growth and fixed mindset students had positive feelings and perceptions of the tour. Originality/value Research on the application value of overseas study tours in helping students from self-financing tertiary institutions develop a growth mindset is scarce, and thus warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".