Robotic Simulation in STEM Education: A Conceptual Framework for Developing Problem-Solving and Systems Thinking Skills (RSiSTEM Framework)
Bibliographic record
Abstract
This study aimed to design, develop, and validate the RSiSTEM framework, a robotics-based simulation learning model intended to foster students’ problem-solving and systems thinking competencies within STEM education. The research followed a two-phase developmental design. In Phase 1, the framework was constructed through a systematic synthesis of literature in STEM pedagogy, educational robotics, and simulation-based learning. Phase 2 focused on expert validation using a 5-point Likert scale. A purposive sample of seven experts with backgrounds in educational technology, instructional design, STEM education, and creativity and innovation in higher education participated in the evaluation. The assessment encompassed five domains: conceptual principles, alignment with problem-solving objectives, alignment with systems thinking objectives, feasibility of implementation, and suitability within the STEM education context. Descriptive statistics, including means and standard deviations, were employed to interpret expert judgments. Results indicated consistently high levels of appropriateness across all domains, with overall mean scores ranging from 4.66 to 4.71. The highest rating was observed for feasibility (x̄ = 4.71, SD = 0.42), while adaptability to diverse learner levels (x̄ = 4.29, SD = 0.70) reflected some variability among reviewers. The findings confirm that the RSiSTEM framework is conceptually robust, pedagogically sound, and practically feasible for classroom application. By integrating robotics simulation into structured STEM instruction, the framework offers a validated approach to cultivating higher-order cognitive skills essential for addressing complex challenges in the 21st century.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".