The Propose of an Instructional Model Based on STEM Education Approach for Enhancing the Information and Communication Technology Skills for Elementary Students in Thailand
Bibliographic record
Abstract
The purpose of this research is to propose an instructional model based on Science, Technology, Engineering, and Math (STEM) Education approach for enhancing the information and communication technology skills for elementary students in Thailand. The study was conducted by research and development design and divided into two phases: Phase I is to create a tentative model that was synthesized using the relevant documents and researches concerning the elements and steps of the model. The data was collected through interviewing eight teachers who are experts in STEM education and twenty-four students who were instructed by STEM approach. Phase II consists of proposing a tentative model to eleven experts, evaluating the model, and acquiring an approval of the model by five professionals. The findings of the research are as follows: 1) This model consists of five main elements—principles, purpose, content, teaching and learning process and measurement and evaluation of ICT skills. There are six steps from the engineering design process consisting of problem identification, related information search, solution design, planning and development, testing evaluation, and design improvement and presentation. 2) The eleven experts evaluated the tentative model as appropriate at a high level. The five professionals approved this model as appropriate at a high level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".