Development of Problem-Based Learning Management Activities to Enhance the Knowledge, Skills, and Interests of Students
Bibliographic record
Abstract
China's elderly population is on the rise, and there is a growing need for improved education for professionals in geriatric health care and management. "Age-Friendly Interior Design" is a mandatory component of the curriculum for students majoring in aged care and management. Problem-Based Learning (PBL) has been introduced as an innovative teaching model. The study aims to achieve several objectives:1) To compare the knowledge, skills, and interests of students after using Problem-Based Learning (PBL) against predefined criteria. 2) To compare the knowledge, skills, and interests of students after using the Traditional Teaching Method against predefined criteria, and 3)To compare the knowledge, skills, and interests of students after using Problem-Based Learning (PBL) and the Traditional Teaching Method. This research engaged students pursuing degrees in geriatric nursing and management at a university in Sichuan, China. It encompassed both a control group consisting of 42 students and an experimental group with 48 students. The findings indicated that both PBL and traditional teaching methods had a positive impact on enhancing students' knowledge, skills, and interests to some extent. Problem-based learning (PBL) was more effective in improving these aspects than traditional methods with a significance level of 0.05.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".