Development and Effect of a SnowBall Teaching-Learning Model based on Flipped Learning
Bibliographic record
Abstract
In order to nurture nursing talents with good interest in learning as well as adaptability to the field, it is necessary to have conditions for self-directed learning, this study aimed to the creation of an educational environment and teaching-learning methods; thus, developing a model suitable for nursing students is essential. A snowball teaching-learning model based on flipped learning was developed and applied to nursing students' basic nursing practice classes in order to understand the effect on self-directed learning ability, interpersonal ability, and personality. For the study period, from September 1, 2015 to July 31, 2016, 21 second-year students in the Department of Nursing at University D, located in B city, Busan were recruited through convenience sampling. The collected data were analyzed using SPSS WIN (Ver. 21.0). The results of the study indicated there was a significant difference in the self-directed learning ability score from 3.16±0.28 points before the teaching-learning model application to 3.99±0.49 points after the application of the teaching-learning model. There was a significant difference in from 3.67±0.49 points before application to 3.90±0.43 points after application. There was also a significant difference in the personality score, from 3.69±0.49 points before application of the teaching-learning model to 4.06±0.46 points after application. Therefore, since the flipped learning-based snowball teaching-learning model is helpful in improving job competency, repeated experimental studies are suggested to verify the effectiveness.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".