Action Research Implementation in Developing an Open Source and Low Cost Robotic Platform for STEM Education
Bibliographic record
Abstract
The aim of this paper is double: (a) to record the latest theoretical considerations (literature review) in the field of STEM (acronym of Science, Technology, Engineering, Mathematics), Educational Robotics and the Educational Robotic Platforms used in their implementation, and (b) to validate the argumentation on the potential contribution of an Action Research implementation on STEM education with the ultimate goal of designing and developing an "open philosophy", low-cost, hardware and software educational platform for the implementation of STEM and Educational Robotics.This paper is divided into 7 sections: Introduction, STEM Education, Educational Robotics, Problem statement, Action Research, Methodology, and Conclusion.The Introduction introduces the concept and necessity of STEM education approach.STEM Education section reviews recently published scientific literature related to STEM education (literature review) and summarize the pros and barriers of its use in education.Educational Robotics introduces the robotics as an educational tool and presents empirical evidence on its effectiveness.Educational Robot Platforms subsection presents the most popular -along with their main specs-educational robots for STEM and Educational Robotics use.Problem statement section identifies the scientific gap and composes the necessity to implement research (specifically an Action Research) on designing and developing an "open philosophy", low-cost, hardware and software academic platform for the implementation of STEM and Educational Robotics.Action research section reviews recently published scientific literature related to action research.Research Methodology section presents research's proposal development phases and finally, Conclusion summarizes paper's findings.
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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 | 0.065 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".