The Effects of Short Video-Based Instruction on Enhancing Financial Management Ability Among Students at Fuzhou Software Vocational and Technical College
Bibliographic record
Abstract
This study was conducted on students studying financial management-related courses at Fuzhou Software Vocational and Technical College. The research objectives were threefold: (1) To investigate the students’ perception of the effectiveness of short video-based instruction in financial management courses compared to traditional teaching methods. (2) To study the effects of short video-based instruction to improve the financial management ability of college students at Fuzhou Software Vocational and Technical College. (3) To investigate students’ perspectives at Fuzhou Software Vocational and Technical College on using short video-based instruction to enhance their financial management abilities. 100 students were selected using random sampling, divided into two groups 50. The study employed quantitative analysis, average, and standard deviation for data interpretation. The primary research instruments were questionnaires, lesson plans, and a perspective questionnaire. The results show that: (1) Short video teaching is more effective than traditional teaching methods in improving the understanding and mastery of financial management concepts and skills. (2) The short video teaching method effectively promotes students’ financial learning. (3) Students’ satisfaction with short video teaching methods in financial management courses showed apparent advantages. Future research could explore short video teaching methods in various educational contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".