Using the Problem-Based Learning in STEM Teaching About Bamboo Toothpick Houses
Bibliographic record
Abstract
In Vietnam, there are currently stable and comprehensive innovations in the field of education. Educational scientists have shifted from knowledge-focused teaching to competency teaching. Since then, there have been more new research directions in teaching than in the past, such as integrated teaching, practical application of mathematics and STEM teaching, etc. In these directions, STEM teaching is a new and broad topic. In particular, there are many teaching methods used in STEM teaching. Some people use project teaching, some use discovery teaching, and some others use cooperative teaching methods. Through the research process, we found that STEM is an integrated area, so we should choose one of the most appropriate ways to approach it. That is a problem-based learning method. How does STEM teaching work with problem-based learning? To illustrate this STEM teaching work, we will use the design and implementation of the model of a bamboo toothpick house at Ho Chi Minh City International College.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".