Does higher endorsement with collaboration lead to better performance on collaborative problem solving? An explanatory item response approach to cross-cultural comparisons
Bibliographic record
Abstract
Introduction: Collaborative problem solving (CPS) is an essential competency in the 21st century. However, the understanding of how cultural background shapes individuals' collaboration awareness and its relationship with collaborative problem-solving skills are underexplored by psychometrics. Methods: This study employs Explanatory Item Response Modeling to examine the impact of cultural orientations on students' endorsement with collaboration in PISA 2015. Results: Results show that students endorsed valuing teamwork more than valuing relationships. Western countries with individualist cultures generally demonstrated higher endorsement with collaboration than Eastern countries with collectivist cultures. China has the highest collaboration endorsement, followed by the US, Canada, Korea, and Japan. In contrast, Japan has the highest CPS assessment scores, followed by Korea, Canada, the US, and China. Discussion: Findings revealed that students from collectivist cultures do not have higher endorsements of collaboration compared with students from individualist cultures. Also, higher endorsement of collaboration does not necessarily lead to better success in the CPS assessment. Further research is needed to understand the gap between students' attitudes towards collaboration and their achievement based on cultural values and schools' CPS training.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".