High school science teachers and their views on the problem-based learning approach: barriers to implementation
Bibliographic record
Abstract
In this research I examined the implementation of the problem-based learning (PBL) approach, an innovation implemented as a part of the science education reform that Quebec, Canada, underwent in the last ten years. Throughout my research, I explore various approaches that three high school science teachers take in implementing PBL into their own teaching of the science curriculum. This research is focused on three detailed case-study of these teachers which includes interviews, classroom observations, co-creation and implementation of PBL units, examination of their concerns about the reform using the Sages of concerns model, and reflective journals. Four main findings emerging from the research are: (1) Teachers teach through some aspects of PBL but are unaware of the explicit mandate preventing them from creating lessons in accordance with this mandate. (2) Teachers are experiencing disconnect between the mandated PBL approach to teaching and the content-based mandatory final examinations. (3) Teachers cite a lack of proper financial resources, insufficient time and training as external barriers to the effective implementation of PBL. (4) Teachers cite personal resistance, lack of knowledge, training, and fear of the innovation as internal barriers. The barriers that teachers encounter emerging in this research can help curriculum developers in Quebec to have a better understanding of how to structure future reforms to ensure they are understood by the teachers. Exams mandated in Quebec should be structured in a way, which is more reflective of the curriculum currently employed, ensuring teachers see the value of the curriculum in relation to how the students will be evaluated.
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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 | 0.023 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".