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Presentación del artículo “Mirando hacia el futuro: Ecosistemas tecnológicos de aprendizaje basados en servicios”

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Francisco José García-Peñalvo at University of Salamanca
Francisco José García-Peñalvo
Ángel Fidalgo Blanco at Universidad Politécnica de Madrid
Ángel Fidalgo Blanco
Ángel Hernández-García at Universidad Politécnica de Madrid
Ángel Hernández-García
María Luisa Sein-Echaluce at University of Zaragoza
María Luisa Sein-Echaluce
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Abstract

Esta es la presentación del artículo “Mirando hacia el futuro: Ecosistemas tecnológicos de aprendizaje basados en servicios” realizada en el CINAIC 2015 celebrado en Madrid (España) los días 14-16 de octubre de 2015. La gran distancia existente entre la tecnología y las metodologías docentes provoca que los nuevos avances tecnológicos no tengan fácil su integración en los contextos y prácticas metodológicas implantados, y que las tecnologías educativas maduras y los métodos educativos aplicados no respondan a las demandas de la sociedad ni al potencial transformador de la tecnología para la mejora del aprendizaje. Esta contribución plantea la necesidad de ofrecer un entorno tecnológico para el soporte de servicios de aprendizaje, el ecosistema educativo, que rompa con las limitaciones tecnológicas y de proceso de las actuales plataformas tecnológicas para conseguir una mejora de los procesos educativos. La propuesta de ecosistema educativo se concreta en 6 líneas de actuación: 1) arquitectura para la implantación de ecosistemas de servicios de aprendizaje; 2) toma de decisiones basadas en analíticas de aprendizaje; 3) sistemas de gestión de conocimiento adaptativos; 4) formación gamificada; 5) porfolios semánticos para la recogida de evidencias de aprendizaje; 6) metodologías educativas que hagan un uso efectivo de los avances tecnológicos en pro de la mejora del aprendizaje.
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Presentación del artículo “Mirando hacia el futuro:
Ecosistemas tecnológicos de aprendizaje
basados en servicios”
Francisco J. García-Peñalvo
Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain
fgarcia@usal.es
Ángel Fidalgo-Blanco
Departamento de Matemática Aplicada y Métodos Informáticos, Universidad Politécnica de
Madrid, Madrid, Spain
angel.fidalgo@upm.es
Faraón Llorens-Largo
Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante,
Alicante, Spain
faraon.llorens@ua.es
Ángel Hernández-García
Departamento de Ingeniería de Organización, Administración de Empresas y Estadística,
Universidad Politécnica de Madrid, Madrid, Spain
angel.hernandez@upm.es
María L. Sein-Echaluce
Departamento de Matemática Aplicada
Universidad de Zaragoza, Zaragoza, Spain
mlsein@unizar.es
Santiago Iglesias-Pradas
Departamento de Ingeniería de Organización, Administración de Empresas y Estadística,
Universidad Politécnica de Madrid, Madrid, Spain
s.iglesias@upm.es
Miguel Á. Conde
Departamento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, León,
Spain
mcong@unileon.es
Marc Alier
Departament d'Enginyeria de Serveis i Sistemes d’Informació, Universitat Politècnica de
Catalunya,
Barcelona, Spain
marc.alier@upc.edu
Resumen
Esta es la presentación del artículo “Mirando hacia el futuro: Ecosistemas tecnológicos
de aprendizaje basados en servicios” realizada en el CINAIC 2015 celebrado en Madrid
(España) los días 14-16 de octubre de 2015.
La gran distancia existente entre la tecnología y las metodologías docentes provoca que
los nuevos avances tecnológicos no tengan fácil su integración en los contextos y
prácticas metodológicas implantados, y que las tecnologías educativas maduras y los
métodos educativos aplicados no respondan a las demandas de la sociedad ni al
potencial transformador de la tecnología para la mejora del aprendizaje. Esta
contribución plantea la necesidad de ofrecer un entorno tecnológico para el soporte de
servicios de aprendizaje, el ecosistema educativo, que rompa con las limitaciones
tecnológicas y de proceso de las actuales plataformas tecnológicas para conseguir una
mejora de los procesos educativos. La propuesta de ecosistema educativo se concreta en
6 líneas de actuación: 1) arquitectura para la implantación de ecosistemas de servicios
de aprendizaje; 2) toma de decisiones basadas en analíticas de aprendizaje; 3) sistemas
de gestión de conocimiento adaptativos; 4) formación gamificada; 5) porfolios
semánticos para la recogida de evidencias de aprendizaje; 6) metodologías educativas
que hagan un uso efectivo de los avances tecnológicos en pro de la mejora del
aprendizaje.
El artículo presentado puede citarse como:
García-Peñalvo, F. J., Hernández-García, Á., Conde-González, M. Á., Fidalgo-Blanco,
Á., Sein-Echaluce Lacleta, M. L., Alier-Forment, M., Llorens-Largo, F., &
Iglesias-Pradas, S. (2015). Mirando hacia el futuro: Ecosistemas tecnológicos de
aprendizaje basados en servicios. In Á. Fidalgo Blanco, M. L. Sein-Echaluce
Lacleta, & F. J. García-Peñalvo (Eds.), La Sociedad del Aprendizaje. Actas del III
Congreso Internacional sobre Aprendizaje, Innovación y Competitividad. CINAIC
2015 (14-16 de Octubre de 2015, Madrid, España) (pp. 553-558). Madrid, Spain:
Fundación General de la Universidad Politécnica de Madrid.
Link to the presentation
http://www.slideshare.net/FaraonLlorens/mirando-hacia-el-futuro-ecosistemas-
tecnolgicos-de-aprendizaje-basados-en-servicios
Keywords
Ecosistemas educativos; Servicios de aprendizaje; Analítica de aprendizaje; Gestión del
conocimiento; Gamificación; Aprendizaje informal; Computación en la nube.
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Enhancing teaching and learning through educational data mining and learning analytics: An issue brief
Technical Report
Full-text available
  • Jan 2014
In data mining and data analytics, tools and techniques once confined to research laboratories are being adopted by forward-looking industries to generate business intelligence for improving decision making. Higher education institutions are beginning to use analytics for improving the services they provide and for increasing student grades and retention. The U.S. Department of Education's National Education Technology Plan, as one part of its model for 21st-century learning powered by technology, envisions ways of using data from online learning systems to improve instruction. With analytics and data mining experiments in education starting to proliferate, sorting out fact from fiction and identifying research possibilitiesand practical applications are not easy. This issue brief is intended to help policymakers and administrators understand how analytics and data mining have been-and can be-applied for educational improvement. At present, educational data mining tends to focus on developing new tools for discovering patterns in data. These patterns are generally about the microconcepts involved in learning: one-digit multiplication, subtraction with carries, and so on. Learning analytics-at least as it is currently contrasted with data mining-focuses on applying tools and techniques at larger scales, such as in courses and at schools and postsecondary institutions. But both disciplines work with patterns and prediction: If we can discern the pattern in the data and make sense of what is happening, we can predict what should come next and take the appropriate action. Educational data mining and learning analytics are used to research and build models in several areas that can influence online learning systems. One area is user modeling, which encompasses what a learner knows, what a learner's behavior and motivation are, what the user experience is like, and how satisfied users are with online learning. At the simplest level, analytics can detect when a student in an online course is going astray and nudge him or her on to a course correction. At the most complex, they hold promise of detecting boredom from patterns of key clicks and redirecting the student's attention. Because these data are gathered in real time, there is a real possibility of continuous improvement via multiple feedback loops that operate at different time scales-immediate to the student for the next problem, daily to the teacher for the next day's teaching, monthly to the principal for judging progress, and annually to the district and state administrators for overall school improvement. The same kinds of data that inform user or learner models can be used to profile users. Profiling as used here means grouping similar users into categories using salient characteristics. These categories then can be used to offer experiences to groups of users or to make recommendations to the users and adaptations to how a system performs. User modeling and profiling are suggestive of real-time adaptations. In contrast, some applications of data mining and analytics are for more experimental purposes. Domain modeling is largely experimental with the goal of understanding how to present a topic and at what level of detail. The study of learning components and instructional principles also uses experimentation to understand what is effective at promoting learning. These examples suggest that the actions from data mining and analytics are always automatic, but that is less often the case. Visual data analyticsclosely involve humans to help make sense of data, from initial pattern detection and model building to sophisticated data dashboards that present data in a way that humans can act upon. K-12 schools and school districts are starting to adopt such institution-level analyses for detecting areas for instructional improvement, setting policies, and measuring results. Making visible students' learning and assessment activities opens up the possibility for students to develop skills in monitoring their own learning and to see directly how their effort improves their success. Teachers gain views into students' performance that help them adapt their teaching or initiate tutoring, tailored assignments, and the like. Robust applications of educational data mining and learning analytics techniques come with costs and challenges. Information technology (IT) departments will understand the costs associated with collecting and storing logged data, while algorithm developers will recognize the computational costs these techniques still require. Another technical challenge is that educational data systems are not interoperable, so bringing together administrative data and classroom-level data remains a challenge. Yet combining these data can give algorithms better predictive power. Combining data about student performance-online tracking, standardized tests, teachergenerated tests-to form one simplified picture of what a student knows can be difficult and must meet acceptable standards for validity. It also requires careful attention to student and teacher privacy and the ethical obligations associated with knowing and acting on student data. Educational data mining and learning analytics have the potential to make visible data that have heretofore gone unseen, unnoticed, and therefore unactionable. To help further the fields and gain value from their practical applications, the recommendations are that educators and administrators: • Develop a culture of using data for making instructional decisions. • Involve IT departments in planning for data collection and use. • Be smart data consumers who ask critical questions about commercial offerings and create demand for the most useful features and uses. • Start with focused areas where data will help, show success, and then expand to new areas. • Communicate with students and parents about where data come from and how the data are used. • Help align state policies with technical requirements for online learning systems.Researchers and software developers are encouraged to: • Conduct research on usability and effectiveness of data displays. • Help instructors be more effective in the classroom with more realtime and data-based decision support tools, including recommendation services. • Continue to research methods for using identified student information where it will help most, anonymizing data when required, and understanding how to align data across different systems. • Understand how to repurpose predictive models developed in one context to another. A final recommendation is to create and continue strong collaboration across research, commercial, and educational sectors. Commercial companies operate on fast development cycles and can produce data useful for research. Districts and schools want properly vetted learning environments. Effective partnerships can help these organizations codesign the best tools.
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Towards Networked Learning Analytics - A Concept and a Tool
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Knowledge Spirals in Higher Education Teaching Innovation
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Mirando hacia el futuro: Ecosistemas tecnológicos de aprendizaje basados en servicios
Conference Paper
Full-text available
  • Oct 2015
Resumen-La gran distancia existente entre la tecnología y las metodologías docentes provoca que los nuevos avances tecnológicos no tengan fácil su integración en los contextos y prácticas metodológicas implantados, y que las tecnologías educativas maduras y los métodos educativos aplicados no respondan a las demandas de la sociedad ni al potencial transformador de la tecnología para la mejora del aprendizaje. Esta contribución plantea la necesidad de ofrecer un entorno tecnológico para el soporte de servicios de aprendizaje, el ecosistema educativo, que rompa con las limitaciones tecnológicas y de proceso de las actuales plataformas tecnológicas para conseguir una mejora de los procesos educativos. La propuesta de ecosistema educativo se concreta en 6 líneas de actuación: 1) arquitectura para la implantación de ecosistemas de servicios de aprendizaje; 2) toma de decisiones basadas en analíticas de aprendizaje; 3) sistemas de gestión de conocimiento adaptativos; 4) formación gamificada; 5) porfolios semánticos para la recogida de evidencias de aprendizaje; 6) metodologías educativas que hagan un uso efectivo de los avances tecnológicos en pro de la mejora del aprendizaje. Abstract-The existing distance between technology and learning methods have two consequences: on the one hand, it makes the fit of new technological advances and existing educational methods and practices difficult; on the other hand, mature educational technologies and methods might not give an adequate answer to actual society needs and demands, and they may not fully use their transforming potential to improve learning processes. This study discusses the need for a new technological environment supporting learning services: the educational ecosystem. The educational ecosystems must be able to break the technological constraints of existing learning platforms and achieve an effective improvement of learning processes. Our proposal of educational ecosystems pivots around six specific lines of action: 1) an arquitecture that gives support to learning service-based ecosystems; 2) learning analytics for educational decision making; 3) adaptive knowledge systems; 4) gamifications; 5) semantic porfolios to collect learning evidences; 6) learning methods that make and effective use of technology for the improvement of learning processes.
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Presentation of the paper “Learning services-based technological ecosystems”
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Full-text available
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Una revisión actualizada del concepto de eLearning. Décimo Aniversario
Article
Full-text available
  • Apr 2015
Resumen Abstract Palabras clave: Ecosistemas digitales de aprendizaje; eLearning; mLearning; TIC; LMS; PLE; MOOC; Factor humano; Enseñanza+Aprendizaje; Gestión del conocimiento y de la tecnología. Los continuos avances en el plano tecnológico provocan flujos de innovación-aceptación-consolidación-obsolescencia propios de las estrategias, ya sean ad hoc o planificadas, de gestión del conocimiento y de la tecnología de las corporaciones, y, a otra escala, de los propios individuos. Los procesos de enseñanza+aprendizaje no son, obviamente, ajenos a esta circunstancia. La irrupción de las tecnologías de la Información y la comunicación como herramienta educativa supone un punto de inflexión conceptual y metodológico en la forma en que las instituciones, educativas o no, afrontan los procesos educativos y la gestión del aprendizaje, especialmente en lo tocante al concepto de educación a distancia, que evoluciona, de una manera más o menos significativa, al adoptar Internet como medio, the continuous advances in technology cause innovation-acceptation-consolidation-obsolescence flows regarding the knowledge and technology management strategies, both ad hoc and planned, of the corporations and also, in a different scale, of the individuals. teaching and learning processes are not obviously unaware of this situation. the irruption of Information and communication technologies as educational tools mean both a conceptual and a methodological turning point in the way that institutions, educational or not, face training processes and learning management, especially with regard to the concept of distance education, which evolves, in a more or less significant way, when it adopts Internet as media; that is how the eLearning concept rises. however, from the first eLearning experiences, too much settled
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Technological Ecosystem for Open Education
Article
  • Jan 2014
There are three key aspects that define the current times, and of course, the world of education: learning, digital and open. Those general ideas give support to the Educational Innovation and lead us to propose the basic matrix of our model, which organizes the different elements of what we think an open education model must consider. The main objective of Open Education is autonomous learning and, to achieve it, it is crucial to provide the learners with contents and tools that allow them to conduct their carrier. In the first times of technological tools for education the tendency was to develop monolithic platforms. From our point of view, the key is not uniformity and closed access but integration and open access for heterogeneous information and tools: a technological ecosystem for open education. Following the current trends and the needs of the users, we propose an open, adaptable system, ready to deal with the different user needs (teachers and students) and to the available tools. We propose a technological ecosystem where a community of open educational methods, policies, regulations, applications, and people teams can coexist so that their processes are interrelated and their implementation is based on the physical factors of the technological environment. As an example of application of the proposed model, we analyze the case of the University of Alicante in the period 2005-2012 and we show some indicators that prove the success of this proposal. Finally, some conclusions and proposals for the future are presented.
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