PBLGM Model Through Visual Programming Language (VPL) for Digital Competencies and Problem-Solving Skills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores the application of the Project-Based Learning with Gamification Model (PBLGM) through Visual Programming Language (VPL) to enhance digital competencies and problem-solving skills in learners. The PBLGM model integrates project-based learning and gamification techniques using Kodu Game Lab, aiming to develop essential 21st-century skills. The research involved designing, developing, and evaluating the PBLGM model. Participants included 30 undergraduate learners from Rajamangala University of Technology Tawan-Ok. The study’s findings indicated significant improvements in digital competencies and problem-solving skills post-intervention. The consistency index values ranged between 0.40 and 1.00, with an average value of 0.84. The difficulty values ranged from 0.38 to 0.57, and the reliability value (KR-20) was 0.83. The model effectively enhanced digital competencies and problem-solving skills, as evidenced by improved test scores and positive expert evaluations. This study underscores the importance of integrating gamification and project-based learning in educational contexts to foster critical digital skills.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it