The Development Instructional Model Based on Steam Education by TP-SMART MODEL to Enhance Technological Innovation and Creativity Skills of Secondary Student Mathayom 6
Bibliographic record
Abstract
The Development Instructional Model Based on Steam Education by TP-SMART MODEL To Enhance Technological Innovation and Creativity Skills of Secondary student Mathayom 6, divided into 4 steps. Step 1: Study the current and expected conditions of student learning and innovation skills development. 2) Develop the model 3) Study the results of use 4) Evaluate the results. Sample group used in the study Mathayom 6/4 students at That Phanom School, Semester 1, academic year 2023, 1 classroom, 26 people. By means of Cluster Random Sampling, statistics used include mean (X), standard deviation (S.D.), essential needs index (PNI Modified), and t-test (Match paired t-test) The results of the study found that 1) The results from the synthesis of learning skills and innovation components consisted of 3 components: Including (1) communication and cooperation (2) critical thinking and problem-solving and (3) Creative thinking and innovation Elements with the highest demand index values are creativity and innovation PNIModified=.294. 2) The model development is consistent with values between 0.80-1.00 and the overall average is 0.933. The confirmation results found that the overall components were appropriate at the highest level (X=4.258, S.D.=0.668). 3) Results of the comparative analysis of scores before and after using the format. The difference is statistically significant at .05. 4) Overall evaluation of the use of the model The average is at a high level (X=4.29, S.D.=0.65).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".