Embedding Critical Thinking Pedagogy through Learning Object Design
Bibliographic record
Abstract
In this article, we describe our project involving development of learning objects designed specifically to teach critical thinking in a variety of digital learning environments. The project is part of our on-going efforts to address arguments that such embedded critical thinking (CT) should play a central role within the ecology of 21st Century e-learning environments. The proposed design takes account of the Type I & II characterization of more advanced learning objects that support active learning,better involve students in how things happen, provide an extensive range of acceptable responses, involve creative tasks and require extended periods of time to complete. The pair of projects described here involves developing 12 modular learning objects and supporting videos that address these worthwhile design characteristics and in addition embed the Canadian-based Critical Thinking Consortium's (TC)2 pedagogy of critical thinking. This is accomplished through our proposed Type III design by providing opportunities to engage in critical inquiry about content knowledge, involve students in critical dialogue, and encouraging critical reflection. In addition, the strategies offer the means for teaching other “tools for thought” such as the use of criteria for judgment, habits of mind, and thinking concepts such as attributing causal connections, drawing warranted inferences from statistical data, and interpreting images. We use the first two learning objects, The U-Shape Discussion and The Image Challenger as examples to illustrate use of the objects.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".