Collaborative Curriculum Design in the Context of Financial Literacy Education
Bibliographic record
Abstract
Financial literacy education is being integrated into school curricula at an increasing frequency. However, the majority of teachers lack the required competencies and teacher self-efficacy to effectively teach financial topics. In this study, we evaluated whether participation in teacher design teams (TDTs) results in high-quality educational materials, encouragement of professional learning, and ultimately, enhanced teacher self-efficacy in the face of pending curriculum reform. We conducted an exploratory multiple-case study in Flanders, Belgium. Data were collected from two TDTs that developed materials aligning with the financial literacy learning standards. We observed the team meetings and conducted interviews with the participating teachers and the team coach. Our results suggest that participation in TDTs supports the three outcome variables that we examined. However, they also revealed that each outcome shows room for improvement. Furthermore, the data provided additional evidence for the importance of meeting several input and process factors that had been previously shown to be essential for effective TDT function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".