The Problem-Based Learning Process with A Cloud Learning Environment to Enhance Analysis Thinking
Bibliographic record
Abstract
This study, is aimed at 1) synthesizing the conceptual framework of the problem-based learning process with a cloud learning environment (PBL-CLE process), 2) developing the PBL-CLE process, and 3) studying the result of the development of the PBL-CLE process. The research instruments include 1) the conceptual framework, 2) the PBL-CLE process to enhance analysis thinking, 3) learning achievement, and 4) analysis thinking assessment form. The statistics used in this research are 1) mean, 2) standard deviation, and 3) t-test. The findings reveal that 1) the PBL-CLE process consists of four components: (1) Input includes learning objectives, content, learners, teacher and cloud learning, (2) PBL-CLE process includes problem posing, problem analysis, problem understanding, research procedure, knowledge synthesis, conclusion and evaluation, presentation, and assignment assessment, (3) Output includes analysis thinking, learning achievement, and satisfaction, and (4) Feedback includes analysis thinking and learning achievement; 2) The result of suitability assessment of the PBL-CLE process to enhance analysis thinking is at the highest level; 3) The students’ learning achievement after the implementation of the PBL-CLE process to enhance analysis thinking is significantly higher than that before the implementation at a .01 level of statistical significance; and 4) The result of analysis thinking assessment after the learning process through the PBL-CLE process to enhance analysis thinking is at the very good level.
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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 | 0.003 | 0.007 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".