Development of an Integrated Project-Based Learning Model Focused on Building Values, Attitudes, Skills, and Knowledge (VASK) for Multi-Grade Classrooms
Bibliographic record
Abstract
This study aims to develop an integrated Project-Based Learning (PBL) model based on Values, Attitudes, Skills, and Knowledge (VASK), specifically designed for small-sized schools and mixed-grade classrooms. Employing a phenomenological study approach with a mixed-methods data structure, the research explores two key questions: What are effective multi-grade teaching models? and how does a teacher professional development program (PD) enhance teachers’ ability to implement multi-grade instruction? The study was conducted in collaboration with 45 teachers from five schools across various subjects, including science, mathematics, technology, language, and social studies. It investigates the design and implementation of VASK-based PBL within both classroom and community settings. Data were collected through classroom observations, focus group interviews, and student-teacher reflective journals to assess the model’s effectiveness and adaptability. Findings revealed that the VASK-PBL PD program fosters interdisciplinary learning, community engagement, and student empowerment, enhancing critical thinking, problem-solving, and social responsibility. The study also demonstrated that teachers from all five schools successfully designed multi-grade classroom learning management by integrating inquiry-based learning with context-based education rooted in the local environment. This model offers a practical framework for schools facing resource and staffing limitations, providing meaningful learning experiences that support both academic achievement and social development. Additionally, the study contributes to the PBL literature for mixed-grade settings, highlighting the importance of community-based, values-driven education in promoting equitable learning opportunities for small schools.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| 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".