Measuring Collaborative Problem Solving Using Mathematics-Based Tasks
Why this work is in the frame
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Bibliographic record
Abstract
This study describes an online method of measuring individual students’ collaborative problem-solving abilities using four interactive mathematics-based tasks, with students working in pairs. Process stream data were captured from 3,000 students who completed the tasks in the United States, Australia, Canada, Costa Rica, Singapore, and Finland. The data were transformed into indicators of collaborative problem-solving ability and were analyzed using item response modeling. The assessments employed in this study can be used as a teaching tool for introduction to algebraic concepts and as a measurement instrument for collaborative problem-solving ability. The paper describes the construction, calibration, and reliability of the tasks and considers validation issues, such as fairness between assessments for both partners and avoidance of cultural biases. Investigations into the dependencies between student scores provide evidence for convergent and discriminant validity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it