Exploring Critical Thinking as an Outcome for Students Enrolled in Community Colleges
Bibliographic record
Abstract
Objective: Using data from HEIghten ® Critical Thinking, a student learning outcomes assessment, the purpose of this study was to evaluate what variables are associated with higher critical thinking performance for students enrolled in various community college programs and to evaluate performance differences across demographic and college-level subgroups as well as student perceptions. Method: With data from 1,307 students enrolled across 34 U.S. and Canadian higher education institutions (72% enrolled in 2-year institutions), we utilized a hierarchical regression to identify variables associated with critical thinking performance. Critical thinking performance differences were evaluated using analyses of variance (ANOVAs) and t-tests across student demographic and college experience subgroups and across student perceptions. Results: Results of this study showed (a) consistent significant predictors associated with higher critical thinking performance; (b) a positive relationship between critical thinking performance and the frequency of using critical thinking in college courses; (c) significant, but relatively small performance differences across demographic and college experience subgroups; and (d) positive relationships between student perceptions and critical thinking performance. Conclusion: This study added to the limited literature evaluating critical thinking skills for community college students. Overall, results suggest that institutions should focus attention to the frequency at which students are using critical thinking throughout their courses, which could increase student performance in this particular area, especially if critical thinking is an explicit outcome within the course. Results also suggested the need to emphasize critical thinking skills more across various community college programs and across non-STEM-focused programs. Suggestions for future research are discussed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".