The effect of case nodes in problem-based learning on the length and quality of discussion: a 2x2 factorial study
Bibliographic record
Abstract
Background: Problem-based learning (PBL) relies heavily on case structure for their success. To make more meaningful cases, faculty introduced a “case node” that requires students to make a group decision on the action they will take at a given point in the case. The purpose of this study was to determine whether case nodes enhance PBL discussions. Methods: Two PBL cases were designed with and without a node. In 2011, 2012, and 2015, first-year medical students were assigned one PBL case with a node and one without a node. In total, 26 groups processed cases with a node while 27 groups processed the same cases without the node. All sessions were audio recorded and analyzed to determine the length and quality of discussions. Results: Groups with a node, regardless of case (M = 25.62, SD = 12.25) spent significantly more time in discussion on the node topic than those without a node (M = 16.54, SD = 10.33, p = .005, d = .80). Groups with a node, regardless of case (M = 14.38, SD = 8.04) expressed an opinion significantly more frequently than those without a node (M = 6.07, SD = 5.80, p < .001, d = 1.19). Conclusions: Case nodes increased both the length and depth of discussion on a topic and may be an effective way to enhance case-based instruction.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".