Bibliographic record
Abstract
This article explains the importance of problem-solving skills in STEM education and why insight is an efficient problem solution searching technology.This paper uses the searching inference model to propose insight as a mechanism in heuristic thinking used to help cognitive agents restructure problem formulation and find problem solutions quickly and efficiently.Lastly, through reviewing previous empirical researches, this paper provides the most reasonable assumption about students can use visual-spatial training and mindfulness meditation to help students develop heuristics to solve STEM problems.The visual-spatial training can include the 3D STEM education game and 3D virtual geometry game.Besides, the mindfulness mediation can be training and used to develop mind-flow states, and it also can increases the insight experiences.This paper suggests that future research should increase the sample size and use tools such as fMRI to demonstrate the mechanisms behind heuristic thinking training.As STEM education is relevant to the country's engineering, science, and economics, integrating training of students in problem-solving skills into the education system and developing students' reasoning, decision-making, and problem-solving skills by enhancing the insight experience is worth investigating for the development of STEM education in different countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".