Critical Thinking in the Classroom: Faculty Perspectives and Practices
Bibliographic record
Abstract
This research paper explores critical thinking in higher education from the instructors’ perspective. A customized survey examined how educators perceive, integrate, and evaluate critical thinking within their courses. Using Bloom’s Taxonomy, analysis, and discussion will focus on the views of faculty from various disciplines in a mid-size southeastern university on critical thinking and teaching. The study found that faculty believes they are incorporating critical thinking in their courses and that critical thinking is an essential skill; at the same time, they believe critical thinking is not happening in their classroom. Students’ lack of motivation and understanding of what critical thinking is and how to assess it are some barriers laid out by faculty to justify why they have difficulties incorporating it in the classroom. The lack of a consensus on a definition and not having a standardized assessment tool make the issue even more difficult. In addition, faculty also believe that lack of training and time are significant contributors to worsening the problem. In conclusion, a clear definition of critical thinking and how it should be taught and assessed is needed. In addition, faculty need time and support to develop and integrate critical thinking skills into their teaching.
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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 | 0.029 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".