Similarities and differences in social and emotional profiles among students in Canada, USA, China, and Singapore: PISA 2015
Bibliographic record
Abstract
Although previous research showed that discrete social-emotional skills such as empathy, motivation, and social relationships in school significantly predict achievement, students tend to use various social-emotional skills in combination. As such previous investigations cannot comment on how different combinations or profiles of students’ social-emotional skills predict achievement relative to discrete skills. Likewise, little is known about cross-national comparisons of social-emotional skill profiles (SESP), and the extent to which SESP differ on their academic achievement. The purposes of this study were three-folded: 1) to determine whether a four-factor social-emotional skills model could be used for cross-national comparisons; 2) to identify social-emotional profiles in 15-year-old students from four different countries – Canada, the United States, China, and Singapore; and 3) to evaluate how different profiles predict students’ reading, maths, and collaborative problem-solving (CPS) test scores. Our results showed multigroup measurement invariant in the structure, loadings, and thresholds of the four-factor social-emotional skills model. We identified three profiles labelled Sociable, Reserved and Withdrawn in Canada, Singapore, and the United States; whereas, we found three profiles labelled Solitary, Team-oriented, and Reserved in students in China. Finally, the way each profile associated with reading, maths and CPS in each country appeared to align with the cultural expectations of learning.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".