Measurement of Financial Competence—Designing a Complex Framework Model for a Complex Assessment Instrument
Bibliographic record
Abstract
Financial competence is seen as a complex ability necessary for people to deal with personal financial issues on a daily basis. To foster young peoples’ financial competence via sophisticated and tailored educational programs, the identification of “competence gaps” through complex and authentic assessments is required. While a large number of assessment tools in the field of personal finance already exist, many of them suffer from different shortcomings concerning a competence-oriented approach. Therefore, we present an innovative way to assess students’ financial competence with a complex performance scenario about financial investment. The presented instrument is built on a specifically designed theoretical framework and addresses the need for holistic financial competence measurement. Results of pretesting trials indicate that the instrument is generally capable of measuring young learners’ financial competence, but challenges in scoring remain. Against this background, implications for the instrument’s iterative enhancement are presented and discussed with reference to validity and reliability properties, scoring issues, and statements about the overall feasibility of complex performance tasks in educational settings. The first draft of a scoring scheme is provided. The potential of the instrument in combination with modern technology-based measurement methods (eye tracking, emotion recognition) for competence assessment is described and suggestions for further research are outlined.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".