Using Teacher Dashboards to Assess Group Collaboration in Problem-based Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assessing group collaboration is a critical element in Problem-based Learning (PBL). In asynchronous online PBL settings, facilitators encounter challenges to assess group collaboration because of delayed responses, lack of social cues, and the orchestration load. Teacher dashboards have the potential to support facilitators to assess collaboration by providing synthesized and visualized information about student learning. Previous studies have explored facilitators’ user experience of teacher dashboards. However, little is known about how facilitators with different levels of PBL expertise interpret dashboard information differently. In this study, we analyzed ten PBL facilitators’ utterance moves while interacting with an online teacher dashboard to examine the difference between expert and novice facilitators as they used each visualization. This study can inform the design of teacher dashboards on collaboration assessment.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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