Promoting Student Engagement Through a Critical Thinking Framework in the Elementary Classroom
Bibliographic record
Abstract
Student engagement is an important predecessor of student achievement and social and cognitive development; however, studies over the past two decades show that there has been a severe lack of student engagement in schools. This qualitative research study explores the effects that a critical thinking methodology has on student engagement in the classroom setting. Creating a classroom environment in which students are encouraged to make meaningful connections, by thinking critically and reflecting upon their experiences may help engage today’s young learners. Data for this study was collected through two semi-structured, one-on-one interviews with a classroom teacher and school administrator within a school board in the Greater Toronto Area. The findings of this research suggest that: 1) critical thinking has a positive effect on student and teacher engagement within the classroom; 2) critical thinking has other benefits, including a positive impact on student achievement and students’ higher-order thinking skills, and helps meet the needs of all types of learners; 3) effective strategies must be used to invite and promote critical thinking; 4) there are challenges to implementing critical thinking in a classroom; 5) teachers and administrators need support in order to successfully integrate critical thinking into teaching practices.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".