An Enquiry into Citizenship Education curriculum and pedagogy: the role of technology and student voice.
Bibliographic record
Abstract
The research in this thesis explores Citizenship Education pedagogy at secondary school level in Ontario, Canada. Citizenship Education is a complex subject area and its teaching and learning within the classroom is contentious. The literature indicates the value of student voice and technology; however the ways in which these pedagogical tools can be incorporated into the Citizenship Education classroom have not been explored in great detail. This study uses a Practitioner Inquiry approach within an Action Research model to investigate the research question; how can student voice and technology be used in the engagement of students within the subject area of Citizenship Education in the classroom. The methods developed and used to collect the data for the study served a dual purpose of engaging and empowering the participants within the research and were based on the ethical considerations of researching with young people. The thesis uses an adapted interpretive ecological framework for the conceptualization, interpretation, and analysis of the findings from the study. It provides a rich and detailed description of the context, processes, and considerations that are involved in incorporating student voice and technology within the Citizenship Education classroom through the Action Research design. The results show that student voice and technology can be used pedagogically to help young people construct their own meanings of citizenship and a Critical Citizenship Education framework was developed to support adoption of these approaches more widely. Future directions for research into the use of innovative approaches to the teaching and learning of Citizenship Education in the classroom are considered.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".