Diseño e implantación de un sistema de garantía de calidad para el máster en Gestión y Dirección de Empreses e Instituciones Turísticas (GDEIT) de la Universidad Politécnica de Cartagena (UPCT)
Bibliographic record
Abstract
[SPA]La presente comunciación incluye el diseño y la puesta en marcha de un Sistema de Garantía de Calidad para el Máster en GDEIT de la Universidad Politécnica de Cartagena (UPCT). Dicho sistema de calidad supone el lanzamiento y la mejora contínua de diversas actividades de evaluación del rendimiento de los agentes implicado en el Máster, como el PDI, el PAS, las instalaciones docentes, profesoniales y otros colaboradores externos, asimilación de competencias por parte del alumnado, planificación de las actividades fuera del aula, etc. Para ello, se desarrolla una estrategia de calidad basada en la búsqueda de la excelencia por parte de cada una de las piezas que componen la oferta de posgrado GDEIT. Además, se parte en este diseño de la información disponible relativa a las tres ediciones del Máster ya desarrolladas (dos finalizadas y una en su tramo final), para analizar diversos aspectos relevantes del máster y obtener información sobre la que basar la panificación propuesta: Encuestas COIE para las prácticas, satisfacción, empleabilidad y situación laboral de los egresados, y valoración de los alumnos derivadas de la encuestas de calidad del servicio UPCT. Con todo ello se elabora un informe de la situación inicial y las desviaciones entre la planificación de las actividades propias del posgrado y su realización final. Posteriomente, se diseñan aspectos que completen el cuadro de calidad del máster y aquellas otras acciones destinadas a la mejora contínua del sistema de calidad del mismo, de manera que se dote al posgrado de herramientas autónomas de mejora contínua de la oferta formativa. [ENG]The present paper includes the design and implementation of a Quality Management System for the Master in Toursim 1858 Management of the Technical University of Cartagena (Spain). The quality system includes the launching and continuous improvement of various performance evaluation activities of the agents involved in the programme, such as professors, services personnel, teaching facilities, professionals and other external partners, assimilation of skills by students, planning of activities outside the classroom, etc.. In this way, we develop a quality strategy based on the pursuit of excellence from each of the components of the posgraduate programme in Tourism Managament. Furthermore, this design is based on the available information about the three runned editions of the Master already developed, which forms the basis to analyze all relevant aspects of the master degree: practices in enterprises, surveys on students´ satisfaction, employability and employment status of graduates, and the assessment of pupils arising from the quality of service surveys of the UPCT. With all that collected information we elaborate an initial status report observing the current deviations between the planning of the activities of the graduate and their final completion. Then, we design new instruments directed to accomplish new tasks remaining in our quality map of the master, in order to implement those other actions allowing to continuously improve the quality of the master degree, so as to provide autonomous tools for continuous improvement of the training offer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".