Proporcionar experiencias de aprendizaje ubicuo mediante la combinación de Internet de las Cosas y los estándares de e-Learning

[ES]Actualmente, el aprendizaje está teniendo lugar con mayor frecuencia en cualquier lugar y en cualquier momento. Esto implica que los ambientes del aprendizaje electrónico se expandan desde los entornos de aprendizaje solo virtuales a entornos que implican espacios físicos. Gracias a la evolución de Internet, las TIC (Tecnologías de la Información y Comunicación) y a la Internet de las Cosas, se pueden experimentar nuevos escenarios de aprendizaje por parte de los estudiantes, ya sea individualmente o en colaboración. Estos escenarios de aprendizaje ubicuos, permiten compaginar tanto ambientes virtuales como ambientes físicos. Por tanto, estas experiencias se caracterizan por las interacciones posibles del estudiante con el entorno físico, la detección de los datos contextuales, y también la adaptación de las estrategias pedagógicas y de los servicios según el contexto. Este artículo pretende aprovechar esta tendencia y sustentarla en las normas existentes de aprendizaje electrónico como IMS LD y LOM. La solución propuesta es extender los modelos de normas de aprendizaje electrónico como IMS LD y LOM para soportar Internet de las Cosas y para aportar un enfoque de adaptación de las actividades de aprendizaje según el contexto del estudiante y su huella digital utilizando la API eXperience. En este contexto y con el fin de permitir las capacidades de razonamiento y la interoperabilidad entre los modelos propuestos se proponen representaciones ontológicas y una implementación de la solución. Además, se plantea una arquitectura técnica que resalta los componentes de software necesarios y sus interacciones. Y, por último, se implementa y se evalúa un escenario de aprendizaje ubicuo

Towards the Use of Dialog Systems to Facilitate Inclusive Education

Continuous advances in the development of information technologies have currently led to the possibility of accessing learning contents from anywhere, at anytime, and almost instantaneously. However, accessibility is not always the main objective in the design of educative applications, specifically to facilitate their adoption by disabled people. Different technologies have recently emerged to foster the accessibility of computers and new mobile devices, favoring a more natural communication between the student and the developed educative systems. This chapter describes innovative uses of multimodal dialog systems in education, with special emphasis in the advantages that they provide for creating inclusive applications and learning activities

Enabling smart learning systems within smart cities using open data

Deploying ad-hoc learning environments to use and represent data from multiple sources and networks and to dynamically respond to user demands could be very expensive and ineffective in the long run. Moreover, most of the available data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards. It is preferable to focus on data availability to choose and develop interoperability strategies suitable for smart learning systems based on open standards and allowing seamless integration of third-party data and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the linked open data platforms and automatic reasoning to effectively handle the vast amount of information and to use data linked queries in the domain of cognitive smart learning systems

Some Research Questions and Results of UC3M in the E-Madrid Excellence Network

32 slides.-- Contributed to: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010.-- Presented by C. Delgado Kloos.Proceedings of: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010Universidad Carlos III de Madrid is one of the six main participating institutions in the eMadrid excellence network, as well as its coordinating partner. In this paper, the network is presented together with some of the main research lines carried out by UC3M. The remaining papers in this session present the work carried out by the other five universities in the consortium.The Excellence Network eMadrid, “Investigación y Desarrollo de Tecnologías para el e-Learning en la Comunidad de Madrid” is being funded by the Madrid Regional Government under grant No. S2009/TIC-1650. In addition, we acknowledge funding from the following research projects: iCoper: “Interoperable Content for Performance in a Competency-driven Society” (eContentPlus Best Practice Network No. ECP-2007-EDU-417007), Learn3: Hacia el Aprendizaje en la 3ª Fase (“Plan Nacional de I+D+I” TIN2008-05163/ TSI), Flexo: “Desarrollo de aprendizaje adaptativo y accesible en sistemas de código abierto” (AVANZA I+D, TSI-020301- 2008-19), España Virtual (CDTI, Ingenio 2010, CENIT, Deimos Space), SOLITE (CYTED 508AC0341), and “Integración vertical de servicios telemáticos de apoyo al aprendizaje en entornos residenciales” (Programa de creación y consolidación de grupos de investigación de la Universidad Carlos III de Madrid).Publicad

Design of a recommender system for web based learning

The design of recommender systems is an ongoing research area where several researchers have devised means of incorporating intelligence in web content systems to be able to provide recommendations to learners on the basis of their learning preferences i.e. based on their learning profiles. The paper discusses the design of such a system based mapped to a content ontology and learner profiles created in the system

Education and generative artificial intelligence. Open challenges, opportunities, and risks in higher education

Keynote at the 14th International Conference on eLearning ELEARNING23, held in Belgrade Metropolitan University, Belgrade, Serbia, on September 28th, 2023. In recent months, the intertwined narratives of education and artificial intelligence (AI) have gained remarkable momentum, framing dialogues on the future of learning and teaching. The potency of generative artificial intelligence (GenAI), particularly in higher education, offers a rich tableau of both promises and perils. This keynote delves into the challenges, opportunities, and risks of such technologies within the ambit of higher education. Between the most promised opportunities, we can underline: Personalised learning pathways: GenAI promises a paradigm shift from one-size-fits-all educational models. Analysing individual student data can generate customised learning materials and study plans catering to each learner's strengths, weaknesses, preferences, and pace. Assisting faculty: Educators can harness these technologies to generate lesson content, identify teaching materials gaps, and offer real-time feedback. This could revolutionise pedagogic strategies, making them more responsive and dynamic. Language translation and globalisation: Generative models can instantaneously translate academic materials into multiple languages, breaking down linguistic barriers and democratising access to knowledge. However, risks are also presented in this new scenario, such as: Over-reliance on technology: The allure of AI might seduce institutions into diminishing the role of human educators. The intangible qualities of mentorship, inspiration, and human connection, which are pivotal in the learning process, might be overshadowed. Data privacy and security: With AI systems analysing student data to provide personalised learning experiences, concerns over data privacy emerge. How institutions store, process, and protect this data from breaches becomes paramount. Ethical dilemmas: The capacity of GenAI to create content poses questions about authorship, authenticity, and credibility. In academic research, for instance, discerning human-generated insights from AI-generated ones can be ethically murky. Finally, higher education decision-makers need to accept AI and GenAI as a reality that now has a considerable impact in the education realm, with a special emphasis on universities. From the higher number of new challenges that universities must face, we put the focus on: Integration with existing systems: The seamless incorporation of AI into higher education's technological ecosystems can be intricate. Institutions must grapple with the logistics of technology adoption, ensuring compatibility and minimal disruption. Bias and representation: AI models are trained on vast amounts of data. If this data is skewed or biased, the AI’s generative capabilities may inadvertently perpetuate or exacerbate existing prejudices, leading to non-inclusive or misrepresentative learning materials. Dependence on proprietary solutions: Large Language Models (LLM) have popularised AI in education with important applications such as ChatGPT or Bard. Universities know that the faculty and the students use these tools. However, the dependence of the third parties introduces ethical, security and privacy issues. The higher education institutions should join initiatives to build up their own models based on fine-tuned open-source LLMs. Depersonalisation of Learning: While AI can customise learning, there’s a risk of reducing education to algorithmic interactions, side-lining the humanistic and relational dimensions of learning. Conclusion: A call for thoughtful integration The confluence of GenAI and higher education is undeniably transformative. It beckons an era where personalised, globally accessible, and highly efficient education might become the norm. However, this journey has challenges and risks that demand meticulous attention. A balanced approach is vital for higher education to benefit from GenAI. Universities must be proactive, not just in harnessing the opportunities AI presents but in pre-emptively addressing its challenges. Ethical considerations, especially concerning bias, data privacy, the collaboration consortiums to create a set of safe fine-tuned models for higher education that will be part of their institutional technological ecosystems, and the potential depersonalisation of education, should be at the forefront of any AI integration strategy. In essence, while generative AI stands as a formidable tool in the arsenal of higher education, its deployment must be thoughtful, ethical, and always in service of enhancing human-centric education, which must comply with universities’ digital transformation strategies. Only then can the true potential of this symbiotic relationship be fully realised