Character Education Implementation in the Financial Literacy-Based Classroom Economy
Bibliographic record
Abstract
This study revolves around examining a classroom structure that is based upon financial literacy in order to teach character education to elementary school students in Grades 4-6. This study provides a rich description of one possible structure, the financial literacy-based classroom, as a pedagogical approach to implement the teaching of both financial literacy and character education simultaneously. The research question is: How can an elementary classroom structured around a financial literacy-based “classroom economy” framework facilitate character education instruction across the curriculum? Through classroom observations and teacher interviews, the study provides a detailed description of the process of two teachers using a financial literacy-based classroom economy to teach character education. Both teachers were observed making use of the classroom economy to engage students in conversations on the topic of character by making connections between classroom experiences and the world outside the classrooms. They integrated the classroom economy throughout the curriculum and used it to engage students in character-related discussions. Although the encountered struggles maintaining the economy due to their busy schedules, they both indicated that their students seemed motivated by the classroom economy when it was used in the classroom.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".