Scripting science inquiry learning in CSCL classrooms
Bibliographic record
Abstract
Research on scripting computer-supported collaborative learning (CSCL) has recently received a lot of attention. However, most findings within this research grew out of studies focusing scripting online collaborative learning activities that often had an asynchronous nature and were conducted in artificial settings. This symposium includes an international set of presenters from Belgium, Canada, Germany, and the USA and brings together four studies that focus on scripting face-to-face “classroom” activities, seeing the “classroom” as a formal physical learning environment. The common denominator of the contributions is that they are all field studies focusing on computer-supported science inquiry learning, aiming to investigate the optimal conditions for organizing these inquiry learning environments. Each paper will present the research context, method, data, and conclusions on how scripting can be implemented to support science inquiry learning. Broader implications of the findings of these studies will be discussed with the audience.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.053 | 0.065 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".