Investigating student collaborative problem-solving competency and science achievement with multilevel modeling: Findings from PISA 2015
Bibliographic record
Abstract
Collaborative problem-solving (CPS) competency is critical for 21st century students. However, reports from the Programme for International Student Assessment (PISA) 2015 have revealed significant deficiencies in this competency among young students globally, indicating a critical need for the cultivation of CPS skills. Therefore, it is essential for educators and researchers to examine the factors that influence CPS competency and understand the potential role of CPS in secondary education. The present study aims to investigate the relationship between collaboration dispositions and students' CPS competency as well as the relationships of CPS competency and inquiry-based science instruction (IBSI) with science achievement using the PISA 2015 data. A total of 408,148 students from 52 countries and economies (i.e., regions) were included in our analysis. Unlike most previous studies that only investigated one country at a time and neglected the multilevel data structure of PISA, this study provided a global view through adopting multilevel modeling to account for the cluster effect at the school and country levels. Our findings revealed that valuing relationship was positively associated with CPS, whereas valuing teamwork was negatively associated with CPS. Furthermore, CPS competency was found to be a dominant and positive predictor of science achievement among all study variables, underscoring the importance of integrating CPS into teaching practices to promote student success in science. Additionally, different IBSI activities show varying relationships with science achievement, indicating that caution should be taken when recommending any specific practices associated with IBSI to teachers.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".