Emotional, Attitude and Classroom Action Research Competency Conduction of Undergraduate Students Through STEM Education
Bibliographic record
Abstract
The purposes of this research were: 1) to develop lesson plans for STEM education for undergraduate students with the efficiency of the processing performances and the performance results (E1/E2) at the determining criteria as 75/75, 2) to compare emotional, attitude towards research, and classroom action research competency of undergraduate students before and after implementing STEM education. The sample was one class of the teaching profession program in Chemistry. The experimental group was selected by simple random sampling. The research instrument was lesson plans for STEM education management and practical skill development, a test of research knowledge, a research skill assessment form, an observation form, and a questionnaire. The statistics used were the percentage, mean, standard deviation, and Multivariate Paired Hotelling’s T-Square. The research results showed that 1) the efficiency of the STEM lesson plans for undergraduate students’ processing performances was 87.12 percent, and the performance results were 74.17 percent (87.12/74.17), meeting the set criteria of 75/75, 2) students had emotional, attitude towards research and classroom action research competency conduction after implementing the lesson plans of STEM education was significantly higher than before at the .05 level of statistics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".