21st Century Pedagogies and Citizenship Education: Enacting Elementary School Curriculum Using Critical Inquiry-Based Learning
Bibliographic record
Abstract
How elementary teachers address citizenship is important in 21st century teaching and learning. Situating citizenship education within the varied global contexts of schooling and connecting content to pedagogical approach is a complex task. Even so, citizenship education can be the philosophical underpinning, or vision, for a teaching pedagogy that engages students in active, creative, and critical ways. This chapter illustrates key features and priorities for citizenship education by exploring the concepts of perspective taking, inquiry pedagogy and critical pedagogy and how they work together using the example of elementary school Social Studies in a Canadian context. Using examples from previous studies and narratives from elementary school teachers, this chapter includes portraits of classroom teachers’ work using a critical inquiry-based approach. The chapter illustrates how resources can be used in teachers’ planning to design learning that is nestled in citizenship education. Government curriculum documents as well as scholarly literature and teaching resources can support critical-inquiry for citizenship education. This teaching can lead to active, engaged citizens. There are many approaches to citizenship education; drawing awareness to perspectives and pedagogical possibilities is essential in teacher development. Teacher education is the ideal place for introducing and connecting foundations of education to best practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".