Effectiveness of Critical Thinking Instruction in Higher Education: A Systematic Review of Intervention Studies
Bibliographic record
Abstract
Promoting students’ critical thinking (CT) has been an essential goal of higher education. However, despite the various attempts to make CT a primary focus of higher education, there is little agreement regarding the conditions under which instruction could result in greater CT outcomes. In this review, we systematically examined current empirical evidence and attempted to explain why some instructional interventions result in greater CT gains than others. Thirty three empirical studies were included in the review and features of the interventions of those individual studies were analyzed. Emphasis was given to the study features related to CT instructional approach, teaching strategy, student and teacher related characteristics, and CT measurement. The findings revealed that effectiveness of CT instruction is influenced by conditions in the instructional environment comprising the instructional variables (teaching strategies and CT instructional approaches), and to some extent by student-related variables (year level and prior academic performance). Moreover, the type of CT measures adopted (standardized vs. non-standardized) appear to influence evaluation of the effectiveness of CT interventions. The findings overall indicated that there is a shift towards embedding CT instruction within academic disciplines, but failed to support effectiveness of particular instructional strategies in fostering acquisition and transfer of CT skills. The main limitation in the current empirical evidence is the lack of systematic design of instructional interventions that are in line with empirically valid instructional design principles.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".