Data From Erasmus+ Project Results Platform

Proceedings TEEM 2020: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality.[EN]Collecting data from Erasmus+ projects related to eLearning and the associated methodologies in order to detect those that have been identified as good practice or success story could be very useful in order to help teachers to define success projects in that field. In order to compile projects of interest, we have the Erasmus+ Project Results Platform, which has a very useful information database to locate educational projects that have been funded by the European Union. This database compiles European educational projects that have been developed in Erasmus+ and also in previous programs such as Lifelong Learning Programme since 2007. The advantage of using this tool is that it has a search engine that allows you to search by keywords and has different criteria to filter. It also allows you to export up to a maximum of 1000 projects per search in excel format with basic data from the filtered projects. Therefore, using this tool is key to be able to identify good practices in European educational projects that serve as a reference to find the parameters useful for learning improvement. This article presents the main data collected from the analysis of educational projects that are connected with eLearning and related methodologies in the aforementioned platform. It also defines which ones will be selected to be able to address an adequate analysis that is manageable to carry out the definition of a methodological guide. As a result of the initial analysis, it is considered appropriate to carry out a review of the projects linked to eLearning in KA1 and KA2 actions that have more than 50 projects connected to this topic, involving educational centers, and that are labelled as good practice and / or success story. With the projects that meet these criteria, there is enough information to achieve the objectives of the research in order to be able to design a methodological guide with the key aspects for implementing eLearning projects

Development of a toolkit for a mentoring program

The mentoring kit for a mentoring program provides the mentors with the necessary resources and tools to help to mentees and teams to understand, apply and integrate their strengths in their respective roles. The tools of the kit offer a working model with the mentee to develop an effective strategy that improves his/her performance through development based on strengths. One of the most effective methods for managing and developing talent within students are mentoring programs. These programs provide a vehicle in which knowledge and wisdom is shared while creating an environment for learning and growth. Novice mentors could benefit from a toolkit to help structure effective mentoring programs. This article describes such a toolkit to provide mentors with the necessary resources and tools to help mentees and teams to understand, apply and integrate their strengths in their respective roles. The objectives of this toolkit are: 1. Deliver models and structures so that the mentee has the ability to: - Develop transformational, theoretical and experiential learning processes. - Develop, evaluate and optimize your resources to function with greater creativity, prominence, leadership and proactivity. 2. Stimulate the development of skills that provide innovative perspectives. 3. Learn to apply the tools and skills acquired in the educational field to: - Understand and diagnose situations in context. - Develop intervention plans with his/her mentor adjusted to the needs and expectations of themselves. - Generate spaces for the identification of barriers and conflicts in his/her processes. - Stimulate actions to overcome challenges or opportunities. – Effective accompaniment of the mentees to reach their goals. Additionally, the tools included in the kit offer a working model for the mentee to develop an effective strategy that improves his/her performance through development based on strengths. This article presents the importance of the use of mentoring tools, under the guidelines of the mentoring toolkit design. This article presents the importance of the use of mentoring tools, under the guidelines of the mentoring toolkit design. This paper reflects simple tools that can be used in a systematic way so that in the mentoring process the participant can perform the most difficult task of all, that of investigating themselves and at the same time the mentor can count on valuable data to be able to facilitate the work. Future research will focus on the evaluation of the toolkit

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

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

Improvement of learning through European educational projects

Proceedings TEEM 2020: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality.[EN]The purpose of this article is to set out the research plan for the doctoral thesis, which deals with the definition of a methodological guide for the successful use of digital technologies in education, especially in eLearning, taking as a reference European educational projects that have been successful in achieving an improvement in the teaching and learning process. We live in an increasingly digital society that requires citizens to be prepared to adapt to the needs of the moment and to solve the problems that arise. For this to be possible, the education system must be prepared to adequately train future citizens who will join a changing labor market. To this end, teachers must be trained and know how to carry out efficient educational projects that allow them to make the most of the potential of ICT in the classroom or in distance education. The situation experienced during the 2019-2020 school year with the COVID-19 pandemic has tested the education system and its ability to adapt to a situation where the use of distance education was required and where ICT was very much needed in most of the cases to bring education to the homes. These factors make it very necessary to work for a better teaching professionalization. Therefore, the main objective of this PhD work is to enable teachers to design their projects, involving electronic learning, in a more effective way. To achieve this, what better than to use the educational projects compiled in the Erasmus+ results platform, which allow the analysis of project typology, outcomes, topics and to see those that have been catalogued as a good practice or success story. This database will be a key tool to gather information together with the collaboration of the main actors of those projects that have been successful. A methodological guide would allow teachers and teacher trainers to know the key factors that help to achieve a good design of educational projects and allow an optimal use of ICT resources and the greatest impact on the teaching-learning process

Computational thinking and robotics in education

After the computational thinking sessions in the previous 2016-2018 editions of TEEM Conference, the fourth edition of this track has been organized in the current 2019 edition. Computational thinking is still a very significant topic, especially, but not only, in pre-university education. In this edition, the robotic has a special role in the track, with a strength relationship with the STEM and STEAM education of children at the pre-university levels, seeding the future of our society

Presentation of the paper "Interaction design principles in WYRED platform" in HCII 2017

This is the presentation of the paper entitled “Interaction design principles in WYRED platform” in the Emerging interactive systems for education session at the HCI International 2017 Conference, held in Vancouver, Canada, 9 - 14 July 2017. This work presents the requirements elicitation phase for the WYRED platform. WYRED (netWorked Youth Research for Empowerment in the Digital society) is a European H2020 Project that aims to provide a framework for research in which children and young people can express and explore their perspectives and interests in relation to digital society, but also a platform from which they can communicate their perspectives to other stakeholders effectively through innovative engagement processes. The requirement elicitation is a basic step to design the interactive mechanism to build up the needed social dialog among the involved stakeholders. In order to set up the right interactive tasks, not only functional requirements are elicited, the non-functional requirements play a key role in this project, specially regarding to ensure the security and privacy of the underage people that will be presented in the development of this project

Repositorios del futuro

Intervención en la mesa “Repositorios del futuro”, celebrada en el congreso Ecosistemas del Conocimiento Abierto (ECA 2017) en Salamanca (España) el 27 de octubre de 2017

Una introducción a la inteligencia artificial

Clase del módulo de Ingeniería Informática en el Itinerario “Ciencias Puras” de la Universidad de la Experiencia de la Universidad de Salamanca, impartida el 23 de mayo de 2019 en la Facultad de Geografía e Historia, Universidad de Salamanca. Se presenta una introducción a la inteligencia artificial

Engaging women into STEM in Latin America: W-STEM project

[EN]Significant progress has been made during the last decades to achieve gender equality, but there is still much work to do. In particular, the gender gap is pronounced in the science, technology, engineering, and mathematics (STEM) fields at all levels of education and labour market. In those areas, the women participation remains low, although there are differences from country to country. In the Latin American context, there is a need for carrying out studies to collect quality data about the actual situation of women in STEM. Although some available data show a high proportion of women in Latin American university education, they are a minority in STEM programs. Moreover, this problem is particularly severe in Latin America because of the biases or cultural norms that influence female behaviour. In this context, the W-STEM project seeks to improve strategies and mechanisms for attracting, accessing, and guiding women in Latin America in STEM higher education programs. This work aims to describe the main results to prepare a set of attraction campaigns in secondary schools in the Latin American countries involved in the project (Chile, Colombia, Costa Rica, Ecuador, Mexico). In particular, a self-assessment tool about gender equality in higher education institutions in Latin America, an interview protocol for female role models, and a mobile application to show those role models