Exploring Critical Thinking Disposition of Prospective Educators: Implications for Pedagogical Augmentation
Bibliographic record
Abstract
This research endeavours to explore the critical thinking disposition observed within the cohort of pre-service teachers, subsequently establishing its potential interplay with pertinent demographic variables encompassing academic lineage, high school categorization, and parental employment status. This study delves into the critical thinking disposition of prospective educators within the context of India's National Education Policy (NEP) 2020. This exploration resonates with the NEP's overarching emphasis on fostering multidisciplinary education and innovative pedagogical approaches. By means of investigating the intricate correlations underpinning these dimensions, this study seeks to not only unravel the prevailing level of critical thinking disposition amongst aspiring educators but also to discern any discernible differentials predicated upon the elucidated demographic factors. In doing so, the research aspires to furnish nuanced insights into the putative ramifications of these demographic determinants upon the cognitive proclivities of nascent pedagogues. The research cohort encompasses 170 potential educators drawn from diverse private and public universities, employing a stratified random sampling approach for selection. Data acquisition was carried out through the application of the Florida Critical Disposition Scale (UF/EMI), encompassing three fundamental constructs: Engagement, Innovativeness, and Cognitive Maturity. In adherence to the tenets of quantitative inquiry, the survey methodology was deployed to amass the requisite data. Subsequently, a blend of descriptive statistics, t-test, ANOVA, and point-biserial correlation analysis was enlisted to meticulously scrutinize and interpret the acquired dataset. The study's findings hold potential to enhance teacher education quality, aligning with NEP 2020's focus on professional development and curriculum enrichment, fostering educators adept in cultivating critical thinking skills for dynamic learning environment.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".