The Effect of E-STEM Education on Students’ Perceptions and Engineering Design Process about Environmental Issues
Bibliographic record
Abstract
E-STEM (Environmental, Science, Technology, Engineering and Mathematics) refers to the integration environmental education into STEM education and can have important role on students’ understandings and engineering design process about environmental issues such as plant growth, acid rains, pollution, sustainable agriculture etc. since it engages students in real-world environmental problem-solving that integrates science, technology, engineering, and math. In this manner, the aim of this study was to investigate the effect of E-STEM Education on fifth grade students’ perceptions and their engineering design process about environmental issues. To reach this aim, a one-group pre- and post-test model was used. The research group of study is five 5th grade students at private school, Istanbul, Turkey. The data was collected with open-ended questions, focus group interview and researchers’ observation notes. In data analysis, students’ responses to open-ended questions were analyzed with content analysis and classified in terms of adequacy. Transcribed discussions from focus group interview and researchers’ observation notes were assessed based on Engineering Design Process Framework. As a result of the study, students’ perceptions and engineering design process about environmental issues improved through E-STEM Education.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".