Effects of Teaching Critical Thinking to Saudi Female University Students Using a Stand-Alone Course
Bibliographic record
Abstract
Teaching critical thinking, an educational goal widely discussed in the last 30 years (Halpern, 1993), is an essential element of professional and higher education as it promotes reasoned judgments under ‘conditions of uncertainty,’ a hallmark of professionalism (Levine, 2010; Shulman, 2005; Perry, 1970). In this study, the researchers present the implications of teaching CT in a course format in a Saudi private university that is preparing female professionals for the workforce. The course is taught as part of the general education requirements in the freshman year of study. Female students in the fields of business, computer sciences, and interior design participated in a CT pre-test /post-test sequence given at strategic times throughout the semester. The data illustrate significant improvement in the area of argument identification and analysis, but moderate to low improvement in the other markers of critical thinking. The results not only reflect course instruction as well as other external factors. The study suggests and recommends that in order for students to be critical thinkers, critical thinking would ideally be embedded or integrated throughout the students’ academic career, not just in one, stand-alone course.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".