The Learning Effectiveness of the Innovative Model of Integrating Financial Education into a College Career Planning Course
Bibliographic record
Abstract
This study examines the effectiveness of an innovative model of integrating financial education into a career planning course at the college level. A total of 104 freshman students (48 in the day division and 56 in the evening division) were sampled. Based on the analysis of the “College Students Financial Planning Survey Questionnaire” revealed that: 90% of the daytime students and 97% of the evening students agreed on the importance of financial education and financial planning in career planning; daytime and evening students share common perspectives on the importance of financial planning in career planning: to realize their dreams, they need to have an adequate financial foundation to support their daily living expenses, and then to plan their financial management well for future retirement security. There are several items of college students’ financial planning status varies between the different education programs. According to the analysis of various score results from the “Career Planning Course Learning Satisfaction Questionnaire”, there was no significant difference across academic systems in the analysis of satisfaction with the integration of financial education into a career planning course; the three most important overall satisfaction analyses all indicate a high level of satisfaction, including: the overall teaching content, the overall content design of financial education, and the benefit of integrating financial education into the career planning courses for personal career planning. Finally, this study offers some specific suggestions for integrating college financial education into a career planning course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".