Calidad del apoyo para el aprendizaje de las matemáticas en la transición a la Universidad.

[EN] We   report   on   the   development   and validation  of  an  instrument  that measures students’ perceptions of ‘the quality and effectiveness of the learning support’ (for mathematics) during their transition to university. This is achieved through  quantitative  analyses  of students’ survey data – including some predictive modelling with the measure -  complemented  with  insights  from interview data. The construct validation of the measure was performed using the Rasch Rating Scale Model (RSM). Results include fit and category statistics and the construct hierarchy which is presented with  some  extracts  from  interview data. The paper concludes with some educational implications and examples of how this measure can be used to give substantial practical results.[ES] Este artículo muestra el desarrollo y la validación de un instrumento de medida de las percepciones de los estudiantes de secundaria acerca de, la calidad y la eficacia del apoyo para el aprendizaje de las matemáticas, en el proceso de transición a la educación superior. Para ello, se ha llevado a cabo un análisis cuantitativo de los datos obtenidos mediante un estudio de encuesta que, tomando algunos modelos de predicción, ha conjugado otros datos derivados de entrevistas. La validación de constructo de la medida se ha realizado mediante el RSM (Rating Scale Model) de Rasch. Los resultados incluyen estadísticos de ajuste y de categorías, así como la jerarquización del constructo con algunos extractos de los datos de las entrevistas. El artículo finaliza aportando las principales implicaciones educativas que se derivan de este proceso, mostrando ejemplos de cómo esta medida puede ser utilizada para obtener resultados prácticos importantes sobre el apoyo en el aprendizaje de las matemátticas en los procesos de transición educativa.Pampaka, M.; Hutcheson, G.; Williams, J. (2014). Quality of Learning Support for Mathematics in Transition to University. REDU. Revista de Docencia Universitaria. 12(2):97-118. https://doi.org/10.4995/redu.2014.5642OJS97118122Agresti, A. (1996). An Introduction to Categorical Data Analysis. London: John Wiley & Sons, Inc.Alcock, L., Attridge, N., Kenny, S., & Inglis, M. (2014). Achievement and behaviour in undergraduate mathematics: personality is a better predictor than gender. Research in Mathematics Education, 16(1), 1-17.Andrich, D. (1999). Rating Scale Model. In G. N. Masters & J. P. Keeves (Eds.), Advances in Measurement in Educational Research and Assessment (pp. 110 - 121). 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(2013). Enhancing critical analysis and problem-solving skills in undergraduate psychology: An evaluation of a collaborative learning and problem-based learning approach. Australian Journal of Psychology, 65(1), 38-45.Linacre, J. M. (2002). Optimizing Rating Scale Category Effectiveness Journal of Applied Measurement, 3(1), 85-106.Linacre, J. M. (2014). Winsteps® Rasch measurement computer program. Beaverton, Oregon: Winsteps.com.Lopez, W. A. (1996). Communication Validity and Rating Scales. Rasch Measurement Transactions, 10 (1), 482-483.Lowe, H., & Cook, A. (2003). "Mind the Gap: Are students prepared for higher education?". Journal of Further and Higher Education, 27(1), 53-76.Loyens, S. M. M., Gijbels, D., Coertjens, L., & Côté, D. J. (2013). Students' approaches to learning in problem-based learning: Taking into account professional behavior in the tutorial groups, self-study time, and different assessment aspects. Studies in Educational Evaluation, 39(1), 23-32.Messick, S. 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Journal of Applied Measurement, 1(1), 83-10

Pedagogía de la Innovación – aprendiendo con métodos activos multidisciplinares

[EN] Traditionally, the role of education has been to give knowledge‐based readiness, which later would be applied in practice to various innovation processes in working life. Innovation pedagogy introduces how the development of students' innovation skills from the very beginning of their studies can become possible. The core of innovation pedagogy lies in emphasising interactive dialogue between the educational organization, students, and surrounding working life and society. It aims to develop the student’s innovation competencies which are the learning outcomes which refer to knowledge, skills and attitudes needed for the innovation activities to be successful. It is defined as a learning approach that defines in a new way how knowledge is assimilated, produced and used in a manner that can create innovations. The purpose of this study is to first present the concept of innovation pedagogy and later give examples of the methods used in Turku University of Applied Sciences to implement this pedagogical concept into the everyday life of the university. Innovation pedagogy has been developed in Turku University of Applied Sciences where it also forms part of the strategy in the university. The multidisciplinary projects of applied research and development respond to the customer needs and are integrated with education in a flexible way.[ES] Tradicionalmente, el papel de la educación ha sido el de proporcionar una preparación basada en el conocimiento, que más tarde será aplicada en la práctica de diferentes procesos de innovación en la vida laboral. Pedagogía de la Innovación presenta cómo puede llegar a ser posible el desarrollo de habilidades de innovación en los estudiantes desde el comienzo de sus estudios. El núcleo de la pedagogía de la innovación radica en el énfasis en el diálogo interactivo entre la organización educativa, los estudiantes, y la vida laboral y sociedad circundante. Su objetivo es desarrollar las competencias de innovación de los estudiantes, que son los resultados de aprendizaje que se refieren a conocimientos, habilidades y actitudes necesarias para que las actividades de innovación tengan éxito. Se define como un enfoque de aprendizaje que define de un modo nuevo cómo es asimilado el conocimiento, producido y utilizado de una manera que puede crear innovaciones. El propósito de este estudio es presentar primero el concepto de la pedagogía de la innovación y más tarde dar ejemplos de los métodos utilizados en la Universidad de Ciencias Aplicadas de Turku a la hora de aplicar este concepto pedagógico en la vida cotidiana de la universidad. Pedagogía de la innovación se ha desarrollado en la Universidad de Turku de Ciencias Aplicadas en la que también forma parte de la estrategia de la universidad. Los proyectos multidisciplinares de investigación aplicada y su desarrollo responden a las necesidades del cliente y son integrados con la educación de una manera flexible.Kairisto-Mertanen, L.; Räsänen, M.; Lehtonen, J.; Lappalainen, H. (2012). Innovation pedagogy – learning through active multidisciplinary methods. REDU. Revista de Docencia Universitaria. 10(1):67-86. https://doi.org/10.4995/redu.2012.6122OJS6786101Brady, L. (1996). Outcome‐based Education: a critique, in The Curriculum Journal, Vol 7 (1), Spring, 5‐16.Buss, D. (2008). Secret Destinations, in Innovations in Education and Teaching International, Vol 45 (3), August, 303‐308.Davies, A. (2002). Writing learning outcomes and assessment criteria in art and design, available at www.arts.ac.uk/docs/citad_learningoutcomes.pdf pp. 522‐529. (Accessed 15 May 2011).Finland's National Innovation Strategy (2008). http://www.tem.fi/files /19704/Kansallinen_innovaatiostrategia_12062008.pdf.Gibbons M., Limoges C., Nowotny H., Schwartzman S., Scott P. & Trow, M. (1994). The New Production of Knowledge. The dynamics of science and research in contemporary societies. London: Sage.Harden, R. M. (2002). Learning outcomes and instructional objectives: is there a difference, in Medical Teacher, Vol. 24 (2), 151‐155.Harden. R. M., Crosby, J. R. & Davis, M. H. (1999). An Introduction to Outcome Based Education, in AMEE Guide No. 14, part 1. Medical TeacherHussey, T. & Smith, P. (2008). Learning outcomes: a conceptual analysis, in Teaching in Higher Education, Vol 13 (1), February, 107‐115.Kairisto‐Mertanen, L.; Kanerva‐Lehto, H.; Penttilä, T. (2009). Kohti innovatiopedagogiikkaa - Uusi lähestymistapa ammattikorkeakoulujen opetukseen ja oppimiseen. Turun ammattikorkeakoulun raportteja 92, Tampereen yliopistopaino, Tampere.Kairisto‐Mertanen, L.; Penttilä, T. & Putkonen, A. (2010). Embedding innovation skills in learning, in Innovation and Entrepreneurship in Universities, ed. Marja‐Liisa Neuvonen‐Rauhala; Series C Articles, reports and other current publications, part 72, Lahti University of Applied Sciences; Tampereen yliopistopaino, Tampere.Kairisto‐Mertanen, L.; Penttilä, T. & Nuotio, J. (2011). On the definition of innovation competencies, in Innovations for Competence Management, Conference proceedings. eds. Torniainen; Ilona, Mahlamäki‐Kultanen, Seija, Nokelainen Petri & Paul Ilsley; Series C, reports and other current publications, part 83, Lahti University of Applied Sciences, Esa print Oy.Kanerva‐Lehto, H.; Lehtonen, J.; Jolkkonen, A. & Riihiranta, J. (2011). Research Hatchery - a Concept for Combining Learning, Development and Research. In Towards Innovation pedagogy. 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Herramientas digitales para la modelización matemática colaborativa en línea

[EN] To enable collaborative modeling activities online digital tools are essential. In this paper we present a holistic and adaptable concept for the development and implementation of modeling activities – which could especially be fruitful in times of homeschooling and distance learning. The concept is based on two digital tools: Jupyter Notebooks and a communication platform with video conferences.We carried out this concept in the context of two types of modeling activities: guided modeling days, where the students work on previously prepared and didactically developed digital learning material, and modeling weeks, in which the students work on open problems from research and industry very freely. In this paper the usage of Jupyter Notebook in modeling activities is presented and illustrated with the example of the optimization of a solar power plant. On top, we share our experiences in online modeling activities with high-school students in Germany.[ES] Para facilitar las actividades de modelización colaborativa en línea, las herramientas digitales son esenciales. En este trabajo presentamos un concepto holístico y adaptable para el desarrollo y la implementación de actividades de modelización – que podría ser especialmente provechoso en tiempos de educación a distancia. El concepto se basa en dos herramientas digitales: Jupyter Notebooks y una plataforma de comunicación con videoconferencia. Realizamos este concepto en el contexto de dos tipos de actividades de modelización matemática: días de modelización guiada, en los que los alumnos trabajan con material de aprendizaje digital previamente preparado y desarrollado didácticamente, y semanas de modelización, en las que los alumnos trabajan en problemas abiertos de la investigación o de la industria de forma libre. Se presenta el uso de Jupyter Notebook en las actividades de modelización y se ilustra con el ejemplo de la optimización de una planta solar. Además, compartimos nuestras experiencias en actividades de modelización en línea con estudiantes de secundaria en Alemania.Schönbrodt, S.; Wohak, K.; Frank, M. (2022). Digital Tools to Enable Collaborative Mathematical Modeling Online. Modelling in Science Education and Learning. 15(1):151-174. https://doi.org/10.4995/msel.2022.16269OJS151174151Blum, W. (2015). Quality Teaching of Mathematical Modelling: What Do We Know, What Can We Do? In S. J. Cho (Ed.), The Proceedings of the 12th International Congress on Mathematical Education (pp. 73-96). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-12688-3_9Blum, W., & Borromeo Ferri, R. (2009). Mathematical Modelling: Can it Be Taught and Learnt? Journal of Mathematical Modelling and Application, 1 (1), 45-58.Blum, W., Galbraith, P., Henn, H.-W., & Niss, M. (2007). Modelling and Applications in Mathematics Education. New York: Springer. https://doi.org/10.1007/978-0-387-29822-1Blum, W., & Lei, D. (2007). How do students and teachers deal with modelling problems? In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical Modelling (ICTMA 12): Education, Engineering and Economics (pp. 222-231). Chichester: Horwood Publishing. https://doi.org/10.1533/9780857099419.5.221Borromeo Ferri, R. (2006, 04). Theoretical and empirical differentiations of phases in the modeling process. ZDM, 38(2), 86-95. doi: 10.1007/BF02655883 https://doi.org/10.1007/BF02655883Bruffee, K. (1995). Sharing Our Toys: Cooperative Learning versus Collaborative Learning. Change, 27 (1), 12-18. https://doi.org/10.1080/00091383.1995.9937722Computer-Based Maths. (n.d.). The Computational Thinking Process Poster. www.computationalthinking.org/helix. (accessed: 2021-01-23)Frank, M., Richter, P., Roeckerath, C., & Schönbrodt, S. (2018). 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Digitale Werkzeuge, Simulationen und mathematisches Modellieren [Digital tools, simulations and mathematical modeling]. In Greefrath, G. and Siller, S. (Ed.), Digitale Werkzeuge, Simulationen und mathematisches Modellieren [Digital tools, simulations and mathematical modeling] (pp. 3-22). Wiesbaden: Springer-Verlag. https://doi.org/10.1007/978-3-658-21940-6_1Golub, J. (1988). Focus on Collaborative Learning. Urbana, Illinois: National Council of Teachers of English.Johnson, D., & Johnson, R. (1989). Cooperation and Competition: Theory and Research. Interaction Book Company.Johnson, D., & Johnson, R. (2014). Using technology to revolutionize cooperative learning: An opinion. Frontiers in Psychology, 5 , 1-3. https://doi.org/10.3389/fpsyg.2014.01156Panitz, T. (1999a). Collaborative versus cooperative learning: A comparison of the two concepts which will help us understand the underlying nature of interactive learning. ERIC Document Reproduction Service No. ED448443.Panitz, T. (1999b). 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Study Approaches of Life Science Students Using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F)

[EN] Students' approaches to learning can vary between students of different ages, genders, years, degrees, or cultural contexts. The aim of this study was to assess the approaches to learning of different students of life science degrees. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) has been used to assess the approaches to learning of 505 students of thirteen different subjects of four different degrees at Universitat Politecnica de Valencia in order to study the factors that influence their approaches. Results show a higher deep approach of the students. Differences were observed between subjects and gender, not related to level (bachelor or master) or year. The item reliability analysis showed a high consistency for the main scales, but not for the secondary scales of the R-SPQ-2F questionnaire. High correlation between the deep and surface scales were observed. These data can provide more information to the teachers, which may help them to develop strategies focused on promoting a deeper approach to learning for the students, more adapted to their subject, level, and year.This research was partially funded by innovation educative projects (PIME/2017/A/016/A and PIME/19-20/168) by Vice-Rectorate for Studies, Quality and Accreditation of Universitat Politecnica de Valencia (UPV, Valencia, Spain).Leiva-Brondo, M.; Cebolla Cornejo, J.; Peiró Barber, RM.; Andrés-Colás, N.; Esteras Gómez, C.; Ferriol Molina, M.; Merle Farinós, HB.... (2020). Study Approaches of Life Science Students Using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F). Education Sciences. 10(7):1-18. https://doi.org/10.3390/educsci10070173S118107Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The Challenges of Defining and Measuring Student Engagement in Science. Educational Psychologist, 50(1), 1-13. doi:10.1080/00461520.2014.1002924Jeong, J. 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Transferencia de técnicas de aprendizaje y mejora del rendimiento en la segmentación semántica profunda de nubes de puntos del patrimonio construido

[EN] The growing availability of three-dimensional (3D) data, such as point clouds, coming from Light Detection and Ranging (LiDAR), Mobile Mapping Systems (MMSs) or Unmanned Aerial Vehicles (UAVs), provides the opportunity to rapidly generate 3D models to support the restoration, conservation, and safeguarding activities of cultural heritage (CH). The so-called scan-to-BIM process can, in fact, benefit from such data, and they can themselves be a source for further analyses or activities on the archaeological and built heritage. There are several ways to exploit this type of data, such as Historic Building Information Modelling (HBIM), mesh creation, rasterisation, classification, and semantic segmentation. The latter, referring to point clouds, is a trending topic not only in the CH domain but also in other fields like autonomous navigation, medicine or retail. Precisely in these sectors, the task of semantic segmentation has been mainly exploited and developed with artificial intelligence techniques. In particular, machine learning (ML) algorithms, and their deep learning (DL) subset, are increasingly applied and have established a solid state-of-the-art in the last half-decade. However, applications of DL techniques on heritage point clouds are still scarce; therefore, we propose to tackle this framework within the built heritage field. Starting from some previous tests with the Dynamic Graph Convolutional Neural Network (DGCNN), in this contribution close attention is paid to: i) the investigation of fine-tuned models, used as a transfer learning technique, ii) the combination of external classifiers, such as Random Forest (RF), with the artificial neural network, and iii) the evaluation of the data augmentation results for the domain-specific ArCH dataset. Finally, after taking into account the main advantages and criticalities, considerations are made on the possibility to profit by this methodology also for non-programming or domain experts.[ES] La creciente disponibilidad de datos tridimensionales (3D), como nubes de puntos, provenientes de la detección de la luz y distancia (LiDAR), sistemas de mapeado móvil (MMS) o vehículos aéreos no tripulados (UAV), brinda la oportunidad de generar rápidamente modelos 3D para apoyar las actividades de restauración, conservación y salvaguardia del patrimonio cultural (CH). El llamado proceso de escaneado-a-BIM puede, de hecho, beneficiarse de dichos datos, y ellos mismos pueden ser una fuente para futuros análisis o actividades sobre el patrimonio arqueológico y el construido. Hay varias formas de explotar este tipo de datos, como el modelado de información de edificios históricos (HBIM), la creación de mallas, la rasterización, la clasificación y la segmentación semántica. Este último, referido a las nubes de puntos, es un tema de máxima actualidad no solo en el dominio del PC sino también en otros campos como la navegación autónoma, la medicina o el comercio minorista. Precisamente en estos sectores, la tarea de la segmentación semántica se ha explotado y desarrollado principalmente con técnicas de inteligencia artificial. En particular, los algoritmos de aprendizaje automático (AA) y su subconjunto de aprendizaje profundo (AP) se aplican cada vez más y han establecido un sólido estado de la técnica en la última media década. Sin embargo, las aplicaciones de las técnicas de AP en las nubes de puntos tradicionales son todavía escasas; por tanto, nos proponemos abordar este marco dentro del ámbito del patrimonio construido. Partiendo de algunas pruebas anteriores con la Red Neural Convolucional de Gráfico Dinámico (DGCNN), en esta contribución se presta atención a: i) la investigación de modelos afinados, utilizados como técnica de aprendizaje por transferencia, ii) la combinación de clasificadores externos, como Random Forest (RF), con la red neuronal artificial, y iii) la evaluación de los resultados de aumentación de datos para el conjunto de datos específico del dominio ArCH. Finalmente, después de tener en cuenta las principales ventajas y criticidades, se hace una consideración sobre la posibilidad de beneficiarse de esta metodología también a expertos no programadores o del campo.Matrone, F.; Martini, M. (2021). Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds. Virtual Archaeology Review. 12(25):73-84. https://doi.org/10.4995/var.2021.15318OJS73841225Armeni, I., Sener, O., Zamir, A. 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Machine learning generalisation across different 3D architectural heritage. ISPRS International Journal of Geo-Information, 9(6), 379. https://doi.org/10.3390/ijgi9060379Grilli, E., Özdemir, E., & Remondino, F. (2019a). Application Of Machine And Deep Learning Strategies For The Classification Of Heritage Point Clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W18, 447-454, 2019. https://doi.org/10.5194/isprs-archives-XLII-4-W18-447-2019Grilli, E., Farella, E. M., Torresani, A., & Remondino, F. (2019b). Geometric features analysis for the classification of cultural heritage point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 541-548, 2019 https://doi.org/10.5194/isprs-archives-XLII-2-W15-541-2019Hackel, T., Savinov, N., Ladicky, L., Wegner, J. D., Schindler, K., & Pollefeys, M. (2017). 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Automatic architectural style recognition. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W16, 171-176 3. https://doi.org/10.3390/app7100992Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., & Remondino, F. (2020a). Comparing machine and deep learning methods for large 3D heritage semantic segmentation. ISPRS International Journal of Geo-Information, 9(9), 535. https://doi.org/10.3390/ijgi9090535Matrone, F., Lingua, A., Pierdicca, R., Malinverni, E. S., Paolanti, M., Grilli, E., Remondino, F., Murtiyoso, A., & Landes, T. (2020b). A benchmark for large-scale heritage point cloud semantic segmentation. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020, 1419-1426. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020Murtiyoso, A., & Grussenmeyer, P. (2019a). 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Segmentación semántica multiclase en la digitalización del patrimonio mueble utilizando técnicas de aprendizaje profundo

[EN] Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. An increasing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years, to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while the acquisition of the images is relatively rapid, it is the processes connected to the data processing that are very time-consuming and require substantial manual involvement of the operator. The development of deep learning-based strategies can be an effective solution to enhance the level of automatism. In the case of the current research, which has been carried out in the framework of the digitisation of a collection of wooden maquettes stored in the ‘Museo Egizio di Torino’ using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset a neural network has been trained to automatically perform a semantic classification with the aim to isolate the maquettes from the background. The proposed methodology has allowed obtaining automatically segmented masks with a high degree of accuracy. The followed workflow is described (as regards acquisition strategies, dataset processing, and neural network training), and the accuracy of the results is evaluated and discussed. In addition, the possibility of performing a multiclass segmentation on the digital images to recognise different categories of objects in the images and define a semantic hierarchy is proposed to perform automatic classification of different elements in the acquired images.[ES] Los procesos de digitalización del patrimonio mueble son cada vez más populares para documentar las obras de arte almacenadas en nuestros museos. En los últimos años se han desarrollado un número creciente de estrategias de adquisición y modelado tridimensional (3D) de estos activos de valor incalculable, que responden de manera eficiente a esta necesidad de documentación y contribuyen a profundizar en el conocimiento de las obras maestras investigadas constantemente por investigadores que operan en muchos trabajos de campo. Hoy en día, una de las soluciones más efectivas está relacionada con el desarrollo de técnicas basadas en imágenes, generalmente conectadas a un enfoque fotogramétrico de estructura-y-movimiento (SfM). Sin embargo, si bien la adquisición de las imágenes es relativamente rápida, son los procesos relacionados con el procesamiento de los datos los que consumen mucho tiempo y requieren una participación manual sustancial del operador. El desarrollo de estrategias basadas en el aprendizaje profundo puede ser una solución eficaz para mejorar el nivel de automatismo. En el caso de la presente investigación, que se ha llevado a cabo en el marco de la digitalización de una colección de maquetas de madera almacenadas en el 'Museo Egizio di Torino' mediante un enfoque fotogramétrico, se propone una estrategia de enmascaramiento automático mediante técnicas de aprendizaje profundo, que incrementa el nivel de automatismo y por tanto optimiza el flujo fotogramétrico. A partir de un conjunto de datos anotados manualmente, se ha entrenado una red neuronal que realiza automáticamente una clasificación semántica con el objetivo de aislar las maquetas del fondo. La metodología propuesta ha permitido obtener más caras segmentadas automáticamente con alto grado de precisión. Se describe el flujo de trabajo seguido (en cuanto a estrategias de toma, procesamiento del conjuntos de datos y entrenamiento de las redes neuronales), y se evalúa y discute la precisión de los resultados. Además, se propone la posibilidad de realizar una segmentación multiclase sobre las imágenes digitales que permitan reconocer diferentes categorías de objetos en las imágenes y definir una jerarquía semántica que clasifique automáticamente diferentes elementos en la toma de las imágenes.The authors thank Volta® A.I. (and in particular Silvio Revelli) for the contribution to this work and for providing high-end hardware for neural network training. In addition, they would like to thank Alessia Fassone of Museo Egizio di Torino and all the people involved in the B.A.C.K. TO T.H.E. F.U.T.U.RE. project (in particular, Fulvio Rinaudo, who coordinated the Geomatic team). Finally, they wish to express their gratitude to Nannina Spanò and Filiberto Chiabrando for the helpful confrontation during the presented research.Patrucco, G.; Setragno, F. (2021). Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques. Virtual Archaeology Review. 12(25):85-98. https://doi.org/10.4995/var.2021.15329OJS85981225Adami, A., Balletti, C., Fassi, F., Fregonese, L., Guerra, F., Taffurelli, L., Vernier, P. (2015). The bust of Francesco II Gonzaga: From digital documentation to 3D printing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5/W3, 9-15. https://doi.org/10.5194/isprsannals-II-5-W3-9-2015Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). 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DIDA Press.Lo Turco, M., Piumatti, P., Rinaudo, F., Calvano, M., Spreafico, A., & Patrucco, G. (2018). The digitisation of museum collections for research, management and enhancement of tangible and intangible heritage. 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 24th International Conference on Virtual Systems & Multimedia (VSMM 2018), San Francisco, CA, USA. https://doi.org/10.1109/DigitalHeritage.2018.8810128Mafrici, N., & Giovannini, E. C. (2020). Digitalizing data: From the historical research to data modelling for a (digital) collection documentation. In M. Lo Turco, E. C. Giovannini, , & N. Mafrici (Eds.), Digital & Documentation. Digital Strategies for Cultural Heritage (Vol. 2, pp. 38-51). Pavia University Press. https://doi.org/10.5194/isprs-archives-XLII-2-W15-519-2019Malik, U. S., Guidi, G. (2018). Massive 3D digitization of sculptures: Methodological approaches for improving efficiency. IOP Conference Series: Material Science and Engineering, 364. https://doi.org/10.1088/1757-899X/364/1/012015Minto, S., & Remondino, F. (2014). Online access and sharing of reality-based 3D models. SCIRES-IT-SCIentific RESearch and Information Technology, 4(2), 17-28. http://doi.org/10.2423/i22394303v4n2p17Patrucco, G., Chiabrando, F., Dondi, P, & Malagodi, M. (2018). Image and range-based 3D acquisition and modeling of popular musical instruments. Proceedings from the Document Academy, 5(2), 9. https://doi.org/10.35492/docam/5/2/9Patrucco, G., Rinaudo, F., & Spreafico, A. (2019). A new handheld scanner for 3D survey of small artifacts: The Stonex F6. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W15, 895-901. https://doi.org/10.5194/isprs-archives-XLII-2-W15-895-2019Pierdicca, R., Paolanti, M., Matrone, F., Martini, M., Morbidoni, C., Malinverni, E. S., Frontoni, E., & Lingua, A. M. (2020). Point cloud semantic segmentation using a deep learning framework for Cultural Heritage. Remote Sensing, 12(6), 1005. https://doi.org/10.3390/rs12061005Salvador-García, E., Viñals, M. J., & García-Valldecabres, J. L. (2020). Potential of HBIM to improve the efficiency of visitor flow management in Heritage sites. Towards smart heritage management. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIV-M-1-2020, 451-456. https://doi.org/10.5194/isprs-archives-XLIV-M-1-2020-451-2020Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48. https://doi.org/10.1186/s40537-019-0197-0Stathopoulou, E. K., & Remondino, F. (2019). Semantic photogrammetry: Boosting image-based 3D reconstruction with semantic labeling. 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The Effect of Edutainment Learning Model on Early Childhood Socio-emotional Development

The idea of edutainment began to become the interest of early childhood educators to make the learning process more holistic, including knowledge about how the brain works, memory, motivation, self-image, emotions, learning styles, and other learning strategies. This study aims to analyse and compare the effect of edutainment and group learning on the socio-emotional development of early childhood. This research method uses a quasi-experimental design with data collection techniques derived from the results of the pre-test and post-test on 20 children. The results of this study indicate that there are differences in the influence of edutainment learning with the control group on the social-emotional development of early childhood. Although both groups affect the socio-emotional development, edutainment learning has a better effect than the control group. For further research, it is recommended to create various types of edutainments learning to improve various aspects of children development. Keywords: Early Childhood, Edutainment Learning Model, Socio-emotional Development References: Afrianti, N. (2018). Permainan Tradisional, Alternatif Media Pengembangan Kompetensi Sosial-Emosi Anak Usia Dini [Traditional Games, Alternative Media for Early Childhood Social-Emotional Competence Development]. Cakrawala Dini: Jurnal Pendidikan Anak Usia Dini, 5(1). https://doi.org/10.17509/cd.v5i1.10405 Alwaely, S. A., Yousif, N. B. A., & Mikhaylov, A. (2021). Emotional development in preschoolers and socialization. Early Child Development and Care, 191(16), 2484–2493. https://doi.org/10.1080/03004430.2020.1717480 Andri Oza, & Zaman, B. (2016). Edutainment dalam Mata Pelajaran Pendidikan Agama Islam. Mudarrisa: Jurnal Kajian Pendidikan Islam, 8(1). https://doi.org/10.18326/mdr.v8i1.117-144 Aubert, A., Molina, S., Schubert, T., & Vidu, A. (2017). Learning and inclusivity via Interactive Groups in early childhood education and care in the Hope school, Spain. Learning, Culture and Social Interaction, 13, 90–103. https://doi.org/10.1016/j.lcsi.2017.03.002 Breaux, R. P., Harvey, E. A., & Lugo-Candelas, C. I. (2016). The Role of Parent Psychopathology in Emotion Socialization. Journal of Abnormal Child Psychology, 44(4), 731–743. PubMed. https://doi.org/10.1007/s10802-015-0062-3 Capurso, M., & Ragni, B. (2016). Bridge Over Troubled Water: Perspective Connections between Coping and Play in Children. Frontiers in Psychology, 7, 1953. https://doi.org/10.3389/fpsyg.2016.01953 Cheng, Y.-J., & Ray, D. C. (2016). Child-Centered Group Play Therapy: Impact on Social-Emotional Assets of Kindergarten Children. The Journal for Specialists in Group Work, 41(3), 209–237. https://doi.org/10.1080/01933922.2016.1197350 Chilingaryan, K., & Zvereva, E. (2020). Edutainment As a New Tool for Development. JAEDU- International E-Journal of Advances in Education, 16, 9. Chiu, M. M., & Chow, B. W. Y. (2011). Classroom Discipline Across Forty-One Countries: School, Economic, and Cultural Differences. Journal of Cross-Cultural Psychology, 42(3), 516–533. https://doi.org/10.1177/0022022110381115 Chung, K. K. H., Lam, C. B., & Liew, J. (2020). Studying Children’s Social-Emotional Development in School and at Home through a Cultural Lens. Early Education and Development, 31(6), 927–929. https://doi.org/10.1080/10409289.2020.1782860 Crescenzi-Lanna, L., & Grané-Oró, M. (2016). An Analysis of the Interaction Design of the Best Educational Apps for Children Aged Zero to Eight = Análisis del diseño interactivo de las mejores apps educativas para niños de ceroa ocho años. Creswell, J. W. (2015). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (Fifth edition). Pearson. Dandashi, A., Karkar, A. G., Saad, S., Barhoumi, Z., Al-Jaam, J., & El Saddik, A. (2015). Enhancing the Cognitive and Learning Skills of Children with Intellectual Disability through Physical Activity and Edutainment Games. International Journal of Distributed Sensor Networks, 11(6), 165165. https://doi.org/10.1155/2015/165165 Denham, S. A. (2006). Social-Emotional Competence as Support for School Readiness: What Is It and How Do We Assess It? Early Education and Development, 17(1), 57–89. https://doi.org/10.1207/s15566935eed1701_4 Eurenius, E., Richter Sundberg, L., Vaezghasemi, M., Silfverdal, S.-A., Ivarsson, A., & Lindkvist, M. (2019). Social-emotional problems among three-year-olds differ based on the child’s gender and custody arrangement. Acta Paediatrica (Oslo, Norway: 1992), 108(6), 1087–1095. PubMed. https://doi.org/10.1111/apa.14668 Goldschmidt, T., & Pedro, A. (2019). Early childhood socio-emotional development indicators: Pre-school teachers’ perceptions. Journal of Psychology in Africa, 29(5), 474–479. https://doi.org/10.1080/14330237.2019.1665887 Guran, A.-M., Cojocar, G. S., & Dioşan, L. S. (2020). Developing smart edutainment for preschoolers: A multidisciplinary approach. Proceedings of the 2nd ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, 20–26. https://doi.org/10.1145/3412453.3423197 Halle, T. G., & Darling-Churchill, K. E. (2016). Review of measures of social and emotional development. Measuring Social and Emotional Development in Early Childhood, 45, 8–18. https://doi.org/10.1016/j.appdev.2016.02.003 Hamada, M., & Tsubaki, M. (2021). Relationship Analysis between Children Interests and Their Positive Emotions for Mobile Libraries’ Community Development in a Tsunami Area. Qualitative and Quantitative Methods in Libraries, 31. Heller, S. S., Rice, J., Boothe, A., Sidell, M., Vaughn, K., Keyes, A., & Nagle, G. (2012). Social-Emotional Development, School Readiness, Teacher–Child Interactions, and Classroom Environment. Early Education & Development, 23(6), 919–944. https://doi.org/10.1080/10409289.2011.626387 Hirsh-Pasek, K., Zosh, J. M., Golinkoff, R. M., Gray, J. H., Robb, M. B., & Kaufman, J. (2015). Putting Education in “Educational” Apps: Lessons from the Science of Learning. Psychological Science in the Public Interest, 16(1), 3–34. https://doi.org/10.1177/1529100615569721 Hurlock, E. B. (2001). Developmental Psychology. McGraw-Hill Education. https://books.google.co.id/books?id=DiovBU8zMA4C Maitner, A. T., Mackie, D. M., Pauketat, J. V. T., & Smith, E. R. (2017). The Impact of Culture and Identity on Emotional Reactions to Insults. Journal of Cross-Cultural Psychology, 48(6), 892–913. https://doi.org/10.1177/0022022117701194 Marcelo, A. K., & Yates, T. M. (2014). Prospective relations among pre-schoolers’ play, coping, and adjustment as moderated by stressful events. Journal of Applied Developmental Psychology, 35(3), 223–233. https://doi.org/10.1016/j.appdev.2014.01.001 McClelland, M. M., & Cameron, C. E. (2011). Self-regulation and academic achievement in elementary school children. New Directions for Child and Adolescent Development, 2011(133), 29–44. https://doi.org/10.1002/cd.302 Mohd Yusof, A., Daniel, E. G. S., Low, W. Y., & Ab. Aziz, K. (2014). Teachers’ perception of mobile edutainment for special needs learners: The Malaysian case. International Journal of Inclusive Education, 18(12), 1237–1246. https://doi.org/10.1080/13603116.2014.885595 Mok, M. M. C. (2019). Social and emotional learning. Educational Psychology, 39(9), 1115–1118. https://doi.org/10.1080/01443410.2019.1654195 Munirah. (2018). Urgensi Pengembangan Sosial dan Emosional Anak Usia Dini. Irfani, 14(1), 19–27. Nasser, I., Miller-Idriss, C., & Alwani, A. (2019). Reconceptualizing Education Transformation in Muslim Societies: The Human Development Approach. The Journal of Education in Muslim Societies, 1(1), 3–25. JSTOR. Nikolayev, M., Reich, S. M., Muskat, T., Tadjbakhsh, N., & Callaghan, M. N. (2021). Review of feedback in edutainment games for preschoolers in the USA. Journal of Children and Media, 15(3), 358–375. https://doi.org/10.1080/17482798.2020.1815227 Nurmalitasari, F. (2015). Perkembangan Sosial Emosi Pada Anak Usia Prasekolah. Psikologi UGM, 23(2). https://doi.org/10.22146/bpsi.10567 Okan, Z. (2003). Edutainment: Is learning at risk? Br. J. Educ. Technol., 34, 255–264. Pojani, D., & Rocco, R. (2020). Edutainment: Role-Playing versus Serious Gaming in Planning Education. Journal of Planning Education and Research, 0739456X2090225. https://doi.org/10.1177/0739456X20902251 Protassova, E. (2021). Emotional development in the educational preschool programs of Soviet and Post-Soviet Times. Russian Journal of Communication, 13(1), 97–109. https://doi.org/10.1080/19409419.2021.1884338 Purwanto, S. (2019). Unsur Pembelajaran Edutainment dalam Quantum Learning. Al-Fikri: Jurnal Studi Dan Penelitian Pendidikan Islam, 2(2). https://doi.org/10.30659/jspi.v2i2.5149 Ren, L., Knoche, L. L., & Edwards, C. P. (2016). The Relation between Chinese Preschoolers’ Social-Emotional Competence and Preacademic Skills. Early Education and Development, 27(7), 875–895. https://doi.org/10.1080/10409289.2016.1151719 Rose-Krasnor, L. (1997). The Nature of Social Competence: A Theoretical Review. Social Development, 6, 111–135. Rusydi, N. A. (2018). Pengaruh Penerapan Metode Edutainment Dalam Pembelajaran Terhadap Hasil Belajar IPS Murid SD Kartika XX-1. Dikdas Matappa: Jurnal Ilmu Pendidikan Dasar, 1(2). https://doi.org/10.31100/dikdas.v1i2.281 Shodiqin, R. (2016). Pembelajaran Berbasis Edutainment [Edutainment-Based Learning]. Jurnal Al-Maqayis, 4(1). https://doi.org/doi:http://dx.doi.org/10.18592/jams.v4i1.792 Sprung, M., Münch, H. M., Harris, P. L., Ebesutani, C., & Hofmann, S. G. (2015). Children’s emotion understanding: A meta-analysis of training studies. Developmental Review, 37, 41–65. https://doi.org/10.1016/j.dr.2015.05.001 Sutherland, S., Stuhr, P. T., Ressler, J., Smith, C., & Wiggin, A. (2019). A Model for Group Processing in Cooperative Learning. Journal of Physical Education, Recreation & Dance, 90(3), 22–26. https://doi.org/10.1080/07303084.2019.1559676 Vygotski, L. S. (2012). Thought and Language. MIT Press. Watanabe, N., Denham, S. A., Jones, N. M., Kobayashi, T., Bassett, H. H., & Ferrier, D. E. (2019). Working Toward Cross-Cultural Adaptation: Preliminary Psychometric Evaluation of the Affect Knowledge Test in Japanese Pre-schoolers. SAGE Open, 9(2), 2158244019846688. https://doi.org/10.1177/2158244019846688 Young, E. L., Moulton, S. E., & Julian, A. (2021). Integrating social-emotional-behavioural screening with early warning indicators in a high school setting. Preventing School Failure: Alternative Education for Children and Youth, 65(3), 255–265. https://doi.org/10.1080/1045988X.2021.189831

A case study exploring oral language choice between the target language and the L1s in mainstream CLIL and EFL secondary education

[EN] This case study explores the purposes for which the target language (TL) and the L1s were used orally by students (N=60) and teachers (N=3) in a mainstream CLIL secondary education context compared to EFL instruction in the Balearic Islands (Spain). Data were gathered by means of questionnaires addressed to students and teachers, oral interviews to instructors and observations of class sessions. The findings show some differences in the languages chosen to speak according to pedagogical functions ¿i.e. planned subject-based discourse¿ and real functions ¿i.e. unplanned discourse such as disciplinary or organizational matters¿ (Chavez 2003), with the TL being much more spoken in the former and with much lesser presence of the TL in the latter, especially in the case of the pupils. Moreover, specialized subject-matter terminology was almost always used in the TL by both the students and the teachers, even when speaking in the L1.Our gratitude goes to the Spanish Ministry of Science and Innovation for their generous funding (FF1 2010-21483-C02-01/02). This research has been conducted within the competitive research group ALLENCAM, funded by the Catalan Government (SGR2005-01086/2009-140). Thanks are also due to all the students and teachers from IES Son Pacs (Palma) involved in this research – especially Antònia Vidal, Catherine Cobb, Maria Cloquell and Antoni Quintana. We appreciate the feedback of anonymous peer reviewers, which contributed to improving this paper.Gené Gil, M.; Juan Garau, M.; Salazar Noguera, J. (2012). A case study exploring oral language choice between the target language and the L1s in mainstream CLIL and EFL secondary education. Revista de Lingüística y Lenguas Aplicadas. 7:133-145. doi:10.4995/rlyla.2012.1129SWORD1331457Birello, M. (2005). La alternancia de lenguas en la clase de Italiano lengua extranjera. Su uso en las interacciones en subgrupos de alumnos adultos en Cataluña. Doctoral thesis. Universitat de Barcelona.Borrull, M. N., Catrain, M., Juan Garau, M., Salazar Noguera, J. and Sánchez, R. (2008). "La enseñanza del inglés como lengua extranjera basada en contenidos. Percepciones del profesorado de Educación Secundaria en las Islas Baleares", In. Revista Electrònica d'Investigació i Innovació Educativa i Socioeducativa 1/0: 105-128, in www.in.uib.cat/pags/volumenes/vol1_num0/borull_otros/index.html [accessed: 28.11.2011].Bruton, A. (2011). "Are the Differences Between CLIL and non-CLIL Groups in Andalusia Due to CLIL? A Reply to Lorenzo, Casal and Moore (2010)", Applied Linguistics 32/2: 236-241. http://dx.doi.org/10.1093/applin/amr007Cenoz, J. (2011). "Multilingualism and Multilingual Education: From Monolingual to Multilingual Perspectives". Paper presented at 2nd Barcelona Summer School on Bilingualism and Multilingualism. Barcelona.Cenoz, J. and Gorter, D. (2011). "Focus On Multilingualism: A Study of Trilingual Writing", The Modern Language Journal 95/3: 356-369. http://dx.doi.org/10.1111/j.1540-4781.2011.01206.xCentro Virtual Cervantes (2009). Diccionario de Términos Clave de ELE. Instituto Cervantes, in http://cvc.cervantes.es/Ensenanza/Biblioteca_Ele/Diccio_Ele/Default.Htm [accessed: 29.11.2011].Chavez, M. (2003). "The Diglossic Foreign-Language Classroom: Learners' Views on L1 and L2 Functions", in C. S. Blyth (ed.) The Sociolinguistics of Foreign-Language Classrooms. Heinle: Thomson, 163-208.Costa, F. (2009). "Code-Switching in CLIL Contexts". Paper Presented at III Trobada sobre Semi-Immersió a Catalunya. I Taula Rodona Internacional sobre Programes AICLE. Bellaterra.Coyle, D., Hood, P. and Marsh, D. (2010). CLIL: Content and Language Integrated Learning. Cambridge: Cambridge University Press.Dalton-Puffer, C. and Nikula, T. (2006). "Pragmatics of Content-Based Instruction: Teacher and Student Directives in Finnish and Austrian Classrooms", Applied Linguistics 27/2: 241-267. http://dx.doi.org/10.1093/applin/aml007Gearon, M. M (2011). "The Bilingual Interactions of Late Partial Immersion French Students during a History Task", International Journal of Bilingual Education and Bilingualism, 14/1: 39-48. http://dx.doi.org/10.1080/13670051003623787Gierlinger, E. M. (2007). "Modular CLIL in Lower Secondary Education: Some Insights from a Research Project in Austria", in C. Dalton-Puffer and U. Smit (eds.) Empirical Perspectives on CLIL Classroom Discourse. Frankfurt am Main: Peter Lang, 79-118.Guasch Boyé, O. and Milian Gubern, M. (1999). "De cómo hablando para escribir se aprende lengua", Textos de Didáctica de la Lengua y la Literatura 20: 50-60.Juan Garau, M. and Salazar Noguera, J. (2009). "La integración de las TIC y la enseñanza basada en contenidos en el aula de lengua inglesa", in J. Salazar Noguera and M. Juan Garau (eds.) Aprendizaje integrado de lengua inglesa y contenidos multiculturales online. Palma: Edicions UIB, 11-22.Marsh, D. and Hartiala, A.- K. (2001). "Dimensions of Content and Language Integrated Learning", in D. Marsh, A. Maljers and A.- K. Hartiala (eds.) Profiling European CLIL Classrooms. Languages Open Doors. Jyväskylä: European Platform for Dutch Education, The Netherlands & University of Jyväskylä, 15-53.Marsh, D.; Marsland, B. and Nikula, T. (1999). "CLIL: A Review of Current Thinking", in D. Marsh and B. Marsland (eds.) CLIL Initiatives for the Millennium. Report on the Ceilink Think- Tank. Jyväskylä: University of Jyväskylä, 34-45.Pastrana Izquierdo, A. (2010). "Language functions in CLIL classrooms: Students' oral production in different classroom activities", Views, Vienna English Working Papers (Current research on CLIL 3) 19/3: 72-82.Pérez Márquez, M. E. (2008). "La enseñanza del inglés. Un año después de la implantación de la enseñanza bilingüe", Aula de Innovación Educativa 168: 17-20.Pérez-Vidal, C. (2002). "Spain", in M. Grenfell (ed.) Modern Languages across the Curriculum. London and New York: Routledge/ Falmer, 114-130.Pérez-Vidal, C. and Juan Garau, M. (2010). "To CLIL or not to CLIL? From Bilingualism to Multilingualism in Catalan/Spanish Communities in Spain", in Y. Ruiz de Zarobe and D. Lasagabaster (eds.) CLIL in Spain: Implementation, Results and Teacher Training. Cambridge: Cambridge Scholar, 115-138.Sampson, A. (in press). "Learner code-switching versus English only", to appear in ELT Journal.Wilhelmer, N. (2010). "Content and language integrated learning (CLIL) in the mathematical setting", Views, Vienna English Working Papers (Current research on CLIL 3) 19/3: 97-103

Revisión. Promesas, peligros y oportunidades de la selección genómica en los programas de mejora genética

[EN] The aim of this work was to review the main challenges and pitfalls of the implementation of genomic selection in the breeding programs of different livestock species. Genomic selection is now one of the main challenges in animal breeding and genetics. Its application could considerably increase the genetic gain in traits of interest. However, the success of its practical implementation depends on the selection scheme characteristics, and these must be studied for each particular case. In dairy cattle, especially in Holsteins, genomic selection is a reality. However, in other livestock species (beef cattle, small ruminants, monogastrics and fish) genomic selection has mainly been used experimentally. The main limitation for its implementation in the mentioned livestock species is the high genotyping costs compared to the low selection value of the candidate. Nevertheless, nowadays the possibility of using single-nucleotide polymorphism (SNP) chips of low density to make genomic selection applications economically feasible is under study. Economic studies may optimize the benefits of genomic selection (GS) to include new traits in the breeding goals. It is evident that genomic selection offers great potential; however, a suitable genotyping strategy and recording system for each case is needed in order to properly exploit it.[ES] El objetivo principal de este trabajo fue revisar las oportunidades y riesgos de la implementación de la selección genómica en las diferentes especies de producción animal. La selección genómica es actualmente uno de los principales retos en mejora genética animal. Su aplicación podría incrementar de forma considerable la tasa de ganancia genética en caracteres de interés. Sin embargo, el éxito de su implementación práctica depende de las particularidades de cada esquema de selección y por tanto debe ser estudiada para cada caso en concreto. En vacuno de leche, especialmente en Holstein, la selección genómica es una realidad. En el resto de especies de producción animal, vacuno carne, pequeños rumiantes, monogástricos y peces, la selección genómica, hasta ahora, se ha utilizado principalmente de manera experimental. El limitante principal para su implementación, común para todas las especies mencionadas, es el alto coste del genotipado en comparación con el bajo valor de los candidatos a la selección. No obstante, se está estudiando actualmente la posibilidad de utilizar chips de baja densidad, de manera que sea económicamente viable su aplicación. Serán necesarios estudios económicos para optimizar las ventajas de la selección genómica a la hora de incluir nuevos caracteres en los objetivos de selección. La selección genómica ofrece muchas posibilidades; sin embargo, para poder aprovecharlas es necesario adecuar la estrategia de genotipado y recolección de datos en cada caso.Ibáñez-Escriche, N.; Gonzalez-Recio, O. (2011). Review. Promises, pitfalls and challenges of genomic selection in breeding program. Spanish Journal of Agricultural Research. 9(2):404-413. https://doi.org/10.5424/sjar/20110902-447-10S40441392Calus, M. P. L., & Veerkamp, R. F. (2007). Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. Journal of Animal Breeding and Genetics, 124(6), 362-368. doi:10.1111/j.1439-0388.2007.00691.xDaetwyler, H. D., Villanueva, B., Bijma, P., & Woolliams, J. A. (2007). Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics, 124(6), 369-376. doi:10.1111/j.1439-0388.2007.00693.xGianola, D., Fernando, R. L., & Stella, A. (2006). Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures. Genetics, 173(3), 1761-1776. doi:10.1534/genetics.105.049510Gjerde, B., Gunnes, K., & Gjedrem, T. (1983). Effect of inbreeding on survival and growth in rainbow trout. Aquaculture, 34(3-4), 327-332. doi:10.1016/0044-8486(83)90212-0Goddard, M. (2008). Genomic selection: prediction of accuracy and maximisation of long term response. Genetica, 136(2), 245-257. doi:10.1007/s10709-008-9308-0González-Recio, O., & Forni, S. (2011). Genome-wide prediction of discrete traits using bayesian regressions and machine learning. Genetics Selection Evolution, 43(1). doi:10.1186/1297-9686-43-7González-Recio, O., Gianola, D., Long, N., Weigel, K. A., Rosa, G. J. M., & Avendaño, S. (2008). Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers. Genetics, 178(4), 2305-2313. doi:10.1534/genetics.107.084293GONZÁLEZ-RECIO, O., WEIGEL, K. 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Neural networks and reinforcement learning in wind turbine control

[EN] Pitch control of wind turbines is complex due to the intrinsic non-linear behavior of these devices, and the external disturbances they are subjected to related to changing wind conditions and other meteorological phenomena. This difficulty is even higher in the case of floating offshore turbines, due to ocean currents and waves. Neural networks and other intelligent control techniques have been proven very useful for the modeling and control of these complex systems. Thus, this paper presents different intelligent control configurations applied to wind turbine pitch control. Direct pitch control based on neural networks and reinforcement learning, and some hybrid control configurations are described. The usefulness of neuro-estimators for the improvement of controllers is also presented. Finally, some of these techniques are used in an application example with a land wind turbine model.[ES] El control del ángulo de las palas de las turbinas eólicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que están sometidas debido a las condiciones cambiantes del viento y otros fenómenos meteorológicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde también les afectan las corrientes marinas y las olas. Las redes neuronales, y otras técnicas del control inteligente, han demostrado ser muy útiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eólicas. Se describe el control directo del ángulo de las palas del aerogenerador y algunas configuraciones híbridas de control. Se expone la utilidad de los neuro-estimadores para la mejora de los controladores. Finalmente, se muestra un ejemplo de aplicación de algunas de estas técnicas en un modelo de turbina terrestre.Ministerio de Ciencia, Innovación y Universidades: Proyecto MCI AEI/FEDER RTI2018- 094902-B-C21Sierra-García, JE.; Santos, M. (2021). Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas. Revista Iberoamericana de Automática e Informática industrial. 18(4):327-335. https://doi.org/10.4995/riai.2021.16111327335184Abouheaf, M., Gueaieb, W., Sharaf, A. 2018. Model-free adaptive learning control scheme for wind turbines with doubly fed induction generators. IET Renewable Power Generation 12(14), 1675-1686. https://doi.org/10.1049/iet-rpg.2018.5353Alvarez-Ramos, C. M., Santos, M., López, V. 2010. Reinforcement learning vs. A* in a role playing game benchmark scenario. 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Hierarchical pitch control for small wind turbines based on fuzzy logic and anticipated wind speed measurement. Applied Sciences, 10(13), 4592. https://doi.org/10.3390/app10134592Chen, P., Han, D., Tan, F., Wang, J. 2020. Reinforcement-based robust variable pitch control of wind turbines. IEEE Access 8, 20493-20502. https://doi.org/10.1109/ACCESS.2020.2968853Demirdelen, T., Tekin, P., Aksu, I. O., Ekinci, F. 2019. The prediction model of characteristics for wind turbines based on meteorological properties using neural network swarm intelligence. Sustainability, 11(17), 4803. https://doi.org/10.3390/su11174803Deng, X., Yang, J., Sun, Y., Song, D., Xiang, X., Ge, X., Joo, Y. H. 2019. Sensorless effective wind speed estimation method based on unknown input disturbance observer and extreme learning machine. Energy, 186, 115790. https://doi.org/10.1016/j.energy.2019.07.120Deng, X., Yang, J., Sun, Y., Song, D., Yang, Y., Joo, Y. H. 2020. 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