Exploring Problem‐based Learning in the Context of High School Science: Design and Implementation Issues
Bibliographic record
Abstract
This paper reports on the experiences of a small collaborative inquiry group consisting of a high school science teacher, Deidre, and two university researchers, the authors of this paper, as they explored an active, inquiry‐based approach to teaching and learning referred to as Problem‐Based Learning or PBL ( Barrows, 1994 ; Barrows & Tamblyn, 1980 ). Although PBL is not new and has an established tradition in medical education and other professional schools, the use and scholarship of PBL at the secondary level is only starting to emerge. This small‐scale collaboration allowed the co‐inquirers to delve into the complexities of PBL and to examine its feasibility as a curriculum and instructional approach in the context of high school science teaching and learning. The three collaborators adopted an action‐based inquiry method referred to as Collaborative Inquiry (CI), a “process consisting of repeated episodes of reflection and action through which a group of peers strives to answer a question of importance to them” ( Bray, Lee, Smith, & Yorks, 2000 , p. 6). Data collection methods and sources included student‐generated work, classroom observation, student interviews, and audio‐taped planning meetings. The outcomes of the study focus on the issues that arose during PBL design and implementation, such as selecting a PBL topic, determining the level of structure to be incorporated into the PBL experience, selecting appropriate assessment approaches, facilitating groups, and providing optimal student feed‐back. In addition, outcomes related to student perceptions of PBL indicated that the majority liked learning through PBL because it promoted active learning, made science relevant, provided variety in learning, and supported group work. The authors discuss implications for the adoption of PBL in K‐12 settings.
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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 | 0.109 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".