Educ@bot: Plataforma educativa eXeLearning para la enseñanza interdisciplinar de la micro-robótica práctica
Bibliographic record
Abstract
[SPA] EDUCABOT es una plataforma de enseñanza basada en la herramienta de software libre eXeLearning que introduce a los estudiantes en el campo de la micro-robótica a través de una completa guía de los aspectos más importantes del sector, las diferentes plataformas, tipos de robots, el hardware, el software, etc., forman parte de esta plataforma. Es una visión panorámica de los principales elementos que nos ofrece el sector, comparando productos y empresas, dando la oportunidad a los alumnos de introducirse en el mundo de la micro-robótica al mismo tiempo que desarrollan, paralelamente, competencias lingüísticas, de trabajo en equipo, de aprender a aprender, etc. eXeLearning nos da soporte en este camino, siendo una herramienta que permite, entre otras bondades, integrar los elementos desarrollados como paquetes SCORM dentro de una plataforma de enseñanza como Moodle. [ENG] Educ@BOT is a web platform based on the open software eXeLearning that introduces to the students in the field of the microrrobotics through and complete guide of the most important aspects of this field, different platforms to be used, different kind of microrobots, hardware, the software, and son on, are included in this platform. It is a panoramic vision of whatever you can find in the main microelectronic devices based on Arduino and focused in the design of different types of microrrobots. You can compare them at the time you improve language skills and the capability of learn to learn. eXeLearning supports this innovation project and integrates all the documentation included, at the time you can convert it in SCORM packets and include the in a learning management platform as Moodle.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".