Empirically Supported Strategies for Encouraging Critical Thinking
Bibliographic record
Abstract
Critical thinking is the ability to construct and evaluate arguments (Facione, 1990). Teaching students to think critically is undeniably one of the most important goals of university education. Accordingly, much of the teaching literature provides suggestions for improving critical thinking among students. Unfortunately, many of these papers contain anecdotal evidence, relying heavily on personal testimony without the support of empirical data and statistical analysis (Abrami et al., 2008; Behar-Horenstein & Niu, 2011). These findings have important implications for instructors who try to foster critical thinking in their classrooms. The present workshop addresses this problem by discussing the following three teaching techniques which have been empirically tested and found to reliably improve critical thinking across multiple investigations: (a) the use of higher-order questioning (Barnett & Francis, 2012; Fenesi, Sana, & Kim, 2014; Renaud & Murray, 2007; Renaud & Murray, 2008; Smith, 1977; Williams, Oliver, & Stockdale, 2004); (b) peer-to-peer interaction (Abrami et al., 2008; Smith, 1997); and (c) explicit critical thinking instruction (Abrami et al., 2008; Bangert-Drowns, & Bankert, 1990; Behar-Horenstein et al., 2010; Tiruneh et al., 2016). This workshop is intended for members of all disciplines seeking to work together to develop an empirically supported framework for teaching critical thinking at the university level.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".