Problem-Based Learning
Bibliographic record
Abstract
Abstract Problem-based learning (PBL) is a student-centered instructional method, with roots in constructivist theory of learning. Since its origin at McMaster University in Canada, PBL has been implemented in numerous programs across many domains and many educational levels worldwide. In PBL, small groups of 10–12 students learn in the context of meaningful problems that describe observable phenomena or events. The PBL process consists of three phases. The first is the initial discussion phase in which the problem at hand is discussed, based on prior knowledge. This initial phase leads to the formulation of learning issues (i.e., questions) that students will answer during the next phase, the self-study phase. Here, they independently select and study a variety of literature resources. During the third and final phase, the reporting phase, students share their findings with each other and critically evaluate the answers to the learning issues. A tutor guides the first and third phase of the process. PBL is based on principles from cognitive and educational psychology that have demonstrated their capacity to foster learning. More specifically, four principles are incorporated in the PBL process: (a) connection to prior knowledge, (b) collaborative learning among students, but also among teachers, (c) gradual development of autonomy, and (d) a focus on the application and transfer of knowledge. Research on the effects of PBL in terms of knowledge acquisition shows that students in traditional, direct instruction curricula tend to perform better on assessments of basic science knowledge. However, differences between PBL students and students in direct instruction classrooms on knowledge tests tend to diminish over time. There is, however, a lack of controlled experiments in this line of PBL research. Directions for future research should focus on combining the best of both direct and student-centered instruction, explore the possibilities of hybrid forms, and investigate how the alignment of scale and didactics of an instructional method could be optimized.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.036 |
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".