Impacts of method courses on Vietnamese pre-service teachers’ perceptions and practices: From the perspectives of model and modeling in STEM education
Bibliographic record
Abstract
Abstract Modeling in visual representations is essential in STEM education because of its concretization in science, technology, engineering, and math learning activities. Therefore, model-based teaching needs to be improved for pre-service teachers (PSTs) to implement STEM education successfully. We conduct the model-based integrated inquiry STEM (MII-STEM) method courses for 16 PSTs in Physics Education in Vietnam. A qualitative analysis was utilized to examine how and to what extent PSTs change in perceptions of models and STEM education. The findings showed that the number of PSTs with a higher understanding of the model increased. PSTs gain a deeper understanding of STEM education and could transfer alternative perceptions of STEM education into STEM lesson plans. PSTs clarified and embedded Science and Engineering Practices in STEM lesson plans. There were changes of PSTs’ STEM lesson plan after the MII-STEM course: (1) product-oriented to process-oriented; (2) make Engineering more apparent; (3) focusing on developing students’ science and engineering practices; (4) define how STEM sub-fields integrated into STEM lesson plans; and (5) using model and modeling in STEM activities. In addition, PSTs had a positive view of the effectiveness of the STEM-focus method course.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".