Application of an Interactive Computer Program to Manage a Problem‐Based Dental Curriculum
Bibliographic record
Abstract
Managing the change from traditional to problem-based learning (PBL) curricula is complex because PBL employs problem cases as the vehicle for learning. Each problem case covers a wide range of different learning issues across many disciplines and is coordinated by different facilitators drawn from the school's multidisciplinary pool. The objective of this project was to adapt an interactive computer program to manage a problem-based dental curriculum. Through application of a commercial database software--CATs (Curriculum Analysis Tools)--an electronic database for all modules of a five-year problem-based program was developed. This involved inputting basic information on each problem case relating to competencies covered, key words (learning objectives), participating faculty, independent study, and homework assignments, as well as inputting information on contact hours. General reports were generated to provide an overview of the curriculum. In addition, competency, key word, manpower, and clock-hour reports at three levels (individual PBL course component, yearly, and the entire curriculum) were produced. Implications and uses of such reports are discussed. The adaptation of electronic technology for managing dental curricula for use in a PBL curriculum has implications for all those involved in managing new-style PBL dental curricula and those who have concerns about managing the PBL process.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".