How Gender and Primary Language Influence the Acquisition of Economic Knowledge of Secondary School Students in the United States and Germany
Bibliographic record
Abstract
Economics has become an essential component of secondary school curricula in many countries as a result of the growing awareness that young adults need fundamental economic knowledge to manage their personal finances. Accordingly, an increasing number of comparative studies are being conducted of commonalities and differences in students’ economic knowledge and its most decisive influencing factors within and across countries. In this study, we compare the performance of secondary school students in the United States (N = 3517) and Germany (N = 983) on the fourth version of the Test of Economic Literacy. We investigate two personal characteristics that have been found to influence the students’ acquisition of economic knowledge: gender and primary language. Although these two characteristics have been considered in numerous studies of economic education in both countries, they have not been investigated together in an international comparison, which would allow more effective pedagogical approaches for economic education to be formulated. We found male students in both countries exhibited greater economic knowledge, and students whose primary language was the same as the national language performed better. We discuss implications for economic education in both countries and cross-nationally.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".