Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills
Bibliographic record
Abstract
This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student. The samples groups are 9 specialists in education, enhancement of problem solving skills, educational technology, and computer and communication technology selected by purposive sampling. Thereafter, researcher analyses the data statistically by examining the mean () and the standard deviation (S.D.). The research result shows that (1) the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student, consists of 4 components which are 1) the analysis of the readiness of the input factors, 2) the process of the PBL-DLL SoMe Model, 3) the evaluation of the achievement of learning and problem solving skills (Output), and 4) the evaluation of the result between evaluating and feedback. (2) The evaluation result of this research project on the PBL-DLL SoMe Model for enhancing problem solving skills of undergraduate in education student shows that 1) the mean () of the results of evaluated input factor readiness is valued at 4.50. The The standard deviation (S.D.) is equal to 0.80. 2) The mean () of the process PBL-DLL SoMe Model is valued at 4.52. The standard deviation (S.D.) is equal to 0.62. 3) The mean () of the evaluation of the achievement of learning and problem solving skill (Output) is valued at 4.62. The standard deviation (S.D.) is equal to 0.72. 4) The mean () of the evaluation between process and feedback is valued at 4.71. The standard deviation (S.D.) is equal to 0.48. 5) The mean () of the overall evaluation of the developed learning model is valued at 4.53. The standard deviation (S.D.) is equal to 0.75. Therefore, the developed learning model is rated as most appropriate in terms of quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".