Implementing an Intervention into a Grade Six Learning Environment: A Design-Based Research Framework
Bibliographic record
Abstract
Educators in some Canadian schools are especially challenged in developing innovative pedagogical approaches that can intellectually engage their students in deep learning of core curriculum content, while equipping them with 21st century competencies. In this paper, we discuss some key ideas on how an intervention, the design and building of digital video games, was implemented and explored, to address this challenge, in four grade 6 social studies classes at a Calgary charter school, utilizing a design-based research framework. Findings revealed that: (i) to effectively implement this intervention in the classroom context, teachers needed to shift/modify their design of instructional activities compared to how they would normally design them in their social studies classes to teach the same chosen content; and (ii) the intervention, as implemented, seem to have the potential to be an effective innovative pedagogy for deep learning and one that promotes the intellectual engagement of students and their development and application of 21st century competencies. Some implications of these findings for the implementation of interventions in school, in terms of transforming the classroom environment, assessing the type of theoretical support needed, using design-based research as an effective framework to study how interventions and developing policy for the implementation of interventions are listed for K-12 educators, school jurisdictions and Alberta Education.
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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 | 0.087 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".