Imagineering Learning With Logical Problem Solving
Bibliographic record
Abstract
This study aimed at developing an imagineering learning process model with logical solutions by using documentary research and relevant experts’ viewpoints with regard to the process of Imagineering Learning—problem-based learning (PBL) involving logical and computational thinking. The data were then synthesized in order to find the relationship of learning theory to achieve an Imagineering Learning process by solving logic problems. The analysis of related documents and research revealed that the Imagineering Learning process involving logical problem solving consisted of 6 important steps as follows: 1) the problem-solving stage, 2) the problem-solving design stage, 3) the innovation development stage, 4) the innovation presentation, 5) the innovation improvement stage, 6) the evaluation stage. The aforementioned learning process can also result in the development of students’ innovative skills, and encouraging learners to develop such skills. The emphasis in terms of the Imagineering process is to create inspiration for the imagination of things that do not yet occur. The process then continues with innovation development by using the PBL process in which students learn solution thinking, focusing on logically-prioritizing problems and their causes and effects. This creates structural and systematic learning through practice, so that students can develop the ability to seek knowledge and develop problem-solving abilities.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".