PBL Core Skills Faculty Development Workshop 3: Understanding PBL Process Assessment and Feedback via Scenario‐Based Discussions, Observation, and Role‐Play
Bibliographic record
Abstract
Tutorial assessment in PBL is thought to be a valid assessment approach and is believed to exert a positive impact on the learning process. Reports, however, have demonstrated that assessment by the facilitator can be unreliable. Training of faculty to conduct this type of assessment has tended to be lacking and is a likely contributor to this inconsistency. This report describes the final in a series of foundation-building faculty development workshops focused on the instructional methodology of PBL. The PBL Assessment and Feedback workshop reported here introduced the theory and practice of conducting process-based assessment accompanied by formative feedback. Scenario-based discussions, mock group demonstration, role-modeling, and role-play were utilized as adult learning-appropriate strategies to familiarize participants with process-based assessment and feedback. Evaluation of the workshop by participants provided evidence that the majority of participants were satisfied with the methods and content of the workshop. Suggestions for additional training in these assessment methods included additional examples, practice, workshops, or observation and mentoring.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".