Empirically Supported Strategies for Encouraging Critical Thinking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it