Bibliographic record
Abstract
Problem-based learning (PBL), first developed for use in a professional setting at McMaster University in Ontario, Canada in the 1960s, is an approach to adult education that has gained widespread acceptance in medical education, most notably in the United States at Harvard Medical School. The goal of PBL is to develop students’ skills at “clinical reasoning” and “self-evaluation and study.” In other words, the problem-based approach emphasizes applied knowledge and aspires to help students learn how to learn. In the spring semester of 2001, I adopted a variant of the PBL approach in my Constitutional Law II course, which covers the Fourteenth Amendment and is an elective open to second- and third-year law students. In the body of this Essay, I first elaborate the concerns that led me to tinker with, and eventually abandon, the Socratic method of teaching in Constitutional Law II. I next briefly describe the various active learning techniques I tried before settling on PBL, and assess them from the standpoint of the concerns that led me to them. Finally, I provide a detailed account of my semester with PBL, and analyze its successes and failures in light of its own internal objectives as well as my and my students’ concerns and goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".