Efficacy of Flipping the College Career Planning Course -- A Finance Career Planning Course
Bibliographic record
Abstract
This study investigates the effectiveness of applying the flipped classroom teaching model to career planning courses in colleges and its efficacy on students’ learning satisfaction and career planning. This study samples 52 first-year university students and 56 first year junior college student. The purpose is to identify and improve the deficiency in the application of flipped classrooms to career planning courses under different academic systems at colleges, so as to enhance benefits to students. In addition, this study adopts the Learning Satisfaction Questionnaire as the assessment tool. The statistical analysis and the research findings suggest that in general, older students (with an average age of 18) in the first year of the four-year technical program are more satisfied with the teaching model of flipped classrooms than younger students (with an average age of 15) in the first year of the five-year junior college program. The teaching model of flipped classrooms has differing effects on students in different academic systems. Based on the research findings, for 4-year technical program freshmen, after taking the flipping career planning course it is about 92% of students have personal specialty-oriented career planning; for first-year junior college students, after taking the flipping career planning course it is about to 86% of students have personal specialty-oriented career planning. This paper develops suggestions for the application of flipped classrooms to the teaching of career planning courses to college students. It serves as a template for the promotion of flipped classroom teaching in higher education for specialty-oriented career planning courses in colleges.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".