Critical Thinking, Active Learning, and the Flipped Classroom: Strategies in Promoting Equity, Inclusion and Social Justice in the B.Ed. Classroom
Bibliographic record
Abstract
In Canada, the national rhetoric of tolerance for diversity oftentimes does not match up to student experiences in the classroom (Dei, Mazzuca, McIsaac, & Zine, 1997). Many students face discrimination because of ethnicity, religious, gender, sexuality, disability, and socioeconomic status. Such discrimination negatively impacts not only students’ ability to perform at high standards, but future economic capital (Harvey & Houle, 2006; Ryan, Pollock, & Antonelli, 2009). The implication for educators in creating more inclusive, socially-just classrooms becomes significant when one looks at Canada’s changing demographic trends (see Eggertson, 2007). It is incumbent that policymakers, researchers, and educators move beyond rhetoric and prepare future teachers with the skills for teaching in Canada’s growing, diverse, and young classrooms.\nThis workshop is designed for instructors who teach in Bachelor of Education programs at any Canadian university. At the same time, it is adaptable to non-Canadian, and/or non-B.Ed. classrooms. The aim is dual and intertwined: to model pedagogy and instruction that instructors can adopt or adapt in teaching for equity and social justice in their own classrooms, and to guide instructors, using stimuli from written and visual text, to interrogate and evaluate their own teaching practices, and re-align them to foster aims of inclusion and social justice. Towards these ends, the workshop employs a triad of strategies, namely critical thinking, active learning, and the flipped classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".