Advancing Skills-Based Education: Designing and Validating a Constructivist Learning Environment Model to Foster Students’ Ill-Structured Problem-Solving and Industrial Competencies
Bibliographic record
Abstract
The overarching aim of this study is to explore how the Constructivist Learning Environment Model enhances ill-structured problem-solving skills and psychomotor competencies among Thai students in skills-based and industrial programs. The research employed a Design and Development approach structured into two phases. Phase 1 involved model development based on literature review, contextual surveys, and theoretical synthesis. Phase 2 focused on model validation through internal expert assessments and external validation via pre-test and post-test with 30 first-year vocational students from Khon Kaen Technological Business College (K-BAC), selected through cluster random sampling. The developed model consists of seven interconnected components: (1) Problem-Based Learning Center, (2) Resource Center, (3) Cognitive Tool Center, (4) Collaboration Center, (5) Problem-Solving Enhancement Center, (6) Scaffolding Center, and (7) Coaching Center. Expert evaluations highlighted the model’s consistency with established theoretical principles, confirming its effectiveness in stimulating cognitive growth, collaborative learning, scaffolding, and problem-solving development. The pre- and post-test results demonstrated significant improvements in students’ problem-solving abilities, with mean scores increasing from 25.7 (SD = 2.85) to 45.27 (SD = 2.35), as indicated by a p-value of 0.000 (p<.05 level of significance). Additionally, the consistency questionnaire revealed 100% student agreement on the coherence of content, media, and design components, confirming the model’s integrated structure. This study contributes a practical, theory-aligned learning environment model for vocational education, emphasizing authentic problem-solving and skill development. Although Phases 1 and 2 findings are promising, further validation is required to confirm the model’s full effectiveness in enhancing vocational and industrial competencies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".