Developing Critically Thoughtful, Media-Rich Lessons in Science: Process
Bibliographic record
Abstract
Abstract: In this paper, I describe a professional development approach and a conceptual framework used to create critically thoughtful and media-rich science learning resources. Greater clarity about the nature of critical thinking and how to support teachers in learning to implement it are needed if we are to respond to broader calls for critical thinking both as a central goal in science education and as a key aspect in the ecology of 21st Century e-learning environments. The conceptual framework is a model of critical thinking developed by the Canadian Critical Thinking Consortium that involves embedding the teaching of five categories of intellectual tools into the teaching of curriculum content. The “tools for thought ” include addressing the need for focused and relevant background knowledge, criteria for judgment, thinking concepts, thinking strategies and the development of habits of mind. The professional development approach engages practicing teachers through focused inquiry groups in collaboration with rich media technicians to develop the e-critical challenges (lessons). Aspects of this “comet approach ” include a series of face-to-face sessions, gradual and planned for introduction to use of laptop computers, developing inquiry oriented writing teams and expert mentorship between large group face-to-face sessions. I explain the unique aspects of both the development process and the challenges in the context of a project involving twelve teachers in the creation of media-rich critical thinking lessons in science for Grade 7 students. Although project assessment data analysis is currently underway, I offer several initial conclusions in relation to
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".