Cooperative learning at the Analytical Chemistry laboratory
Bibliographic record
Abstract
[SPA] El aprendizaje cooperativo es un sistema de organización del trabajo y de motivación, en el que el alumno es responsable de su aprendizaje y el de sus compañeros. A diferencia de los sistemas individualista y competitivo, las metas son grupales. La aplicación sistemática de este sistema da respuesta a tres principios básicos del Espacio Europeo de Educación Superior (EEES): participación y autonomía del estudiante, utilización de metodologías activas y papel del profesorado como agente facilitador del aprendizaje. Brevemente, con esta metodología se divide la clase en pequeños equipos, que reciben unas consignas de actuación a partir de las cuales planifican el trabajo del grupo. Cada miembro del grupo será responsable de áreas específicas que será necesario realizar satisfactoriamente para el éxito del grupo. En esta comunicación se plantea la aplicación del aprendizaje cooperativo para el aprovechamiento efectivo de las clases prácticas de laboratorio de análisis químico, dirigido a alumnos de último curso de la titulación de Ingeniería Química. La práctica en concreto consiste en la verificación de un equipo de HPLC-UV/Vis, para comprobar la conformidad con las especificaciones del fabricante. Además, esta metodología permite trabajar competencias transversales como razonamiento crítico, trabajo en equipo, gestión de proyectos y comunicación entre otras. [ENG] Cooperative learning is a work organizational system to motivate students, who become responsible for their own and their team mates learning. Unlike the individualistic and competitive learning styles, in cooperative learning student achievements occurs only when the other students in the group also achieve the objectives and rewards. Systematic application of this methodology helps reaching three basic principles of the European Higher Education Area (EHEA): student participation and autonomy, active methodologies application and lecturer role as facilitator in the learning process. In short, this methodology involves splitting up the class into small teams, which have to organize their self-workload to complete assignments and reach objectives previously established. Each team member is responsible for specific tasks that must be properly completed for the final success of the group. In this communication we propose the cooperative learning for effective laboratory lessons of analytical chemistry, aimed to last year students of chemical engineering. The specific assignment involves the verification of a whole equipment of liquid chromatography (HPLC) with spectrophotometric detection (UV-Vis) to check compliance with supplier specifications. In addition to specific competences, applying cooperative learning approach, students develop generic competences such as critical thinking, working with other, project management and communication among others.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".