CREAMINKA: An Intelligent Ecosystem for Supporting Management and Information Discovery in Research and Innovation Fields in Universities

This chapter presents a new proposal for supporting the management of research processes in universities and higher education centers. To this aim, the authors have developed a comprehensive ecosystem that implements a knowledge model that addresses three innovative aspects of research: (i) acceleration of knowledge production, (ii) research valorization and (iii) discovery of improbable peers. The ecosystem relies on ontologies and intelligent modules and is able to automatically retrieve information of major scientific databases such as SCOPUS and Science Direct to infer new information. Currently, the system is able to provide guidelines to create improbable research peers as well as automatically generate resilience graphics and reports from more than 17,000 tuples of the ontological database. In this work, the authors describe in detail an important aspect of support systems for research management in higher education: the development and valorization of competences of students collaborating in research process and startUPS of universities. Furthermore, a knowledge model of entrepreneurship (startUPS) as well as an analyzer of general and specific competences based on data mining processes is presented

Developing a strategic approach to MOOCs

During the last eight years, interest in massive open online courses (MOOCs) has grown fast and continuously worldwide. Universities that had never engaged with open or online learning have begun to run courses in these new environments. Millions of learners have joined these courses, many of them new to learning at this level. Amid all this learning and teaching activity, researchers have been busy investigating different aspects of this new phenomenon. In this contribution we look at one substantial body of work, publications on MOOCs that were produced at the 29 UK universities connected to the FutureLearn MOOC platform. Bringing these papers together, and considering them as a body of related work, reveals a set of nine priority areas for MOOC research and development. We suggest that these priority areas could be used to develop a strategic approach to learning at scale. We also show how the papers in this special issue align with these priority areas, forming a basis for future work

Massive Open Online Courses for Employability, Innovation and Entrepreneurship: a Rapid Assessment of Evidence

This report summarises the evidence base regarding the use of open online learning for supporting employability, innovation and entrepreneurship within the European area. It was written as part of the European MOOC Consortium – Labour Markets project (EMC-LM). MOOC platforms in the European MOOC Consortium (EMC) look for solutions to reach better the labour market. This streamlined review of literature draws on scientific literature, project reports, policy documents, case studies and other resources to describe the potential for MOOCs to stimulate and empower organisations to use open education as part of their programs of continuous education (CE) and continuous professional development (CPD) or continuous vocational training (CVT). This report identifies several interstices where the worlds of higher education, vocational educational, training and open online learning come are converging. Evidence is provided for the contention that, through enhancing opportunities for flexible delivery of education, MOOC can innovate the way that we approach degree programmes, lifelong learning, CE and CPD. The information gathered in this report aims to provide a comprehensive overview of relevant material but does not claim to be exhaustive. This report was written as part of the EMC-LM project as a way to establish a shared understanding of the possibilities for collaboration and innovation. The references provided are nonetheless a great starting point for any investigation of the potential of MOOCs for workplace and lifelong learning. Approaches which emphasize the flexible delivery of learning are especially suited to workplace upskilling. For greatest impact and relevance, collaboration should cross disciplinary and professional boundaries, involving a wide range of stakeholders. Employability can be supported through improved management of work transitions; more flexible training options; new routes between education and work; building credibility in CPD; enhancing soft and transversal skills; and developing new mechanisms for authenticating non-formal learning. Innovation can be encouraged by working with greater transparency and sharing; improved dialogue between stakeholders; adopting a reflective attitude towards technology; and through policies which support and motivate new approaches. Entrepreneurship can be moving beyond the knowledge transfer model of entrepreneurial education; drawing stronger connections between theory and practice; promoting collaboration between researchers and practitioners in education and entrepreneurship; and encouraging entrepreneurial culture

Automatic generation of software interfaces for supporting decisionmaking processes. An application of domain engineering & machine learning

[EN] Data analysis is a key process to foster knowledge generation in particular domains or fields of study. With a strong informative foundation derived from the analysis of collected data, decision-makers can make strategic choices with the aim of obtaining valuable benefits in their specific areas of action. However, given the steady growth of data volumes, data analysis needs to rely on powerful tools to enable knowledge extraction. Information dashboards offer a software solution to analyze large volumes of data visually to identify patterns and relations and make decisions according to the presented information. But decision-makers may have different goals and, consequently, different necessities regarding their dashboards. Moreover, the variety of data sources, structures, and domains can hamper the design and implementation of these tools. This Ph.D. Thesis tackles the challenge of improving the development process of information dashboards and data visualizations while enhancing their quality and features in terms of personalization, usability, and flexibility, among others. Several research activities have been carried out to support this thesis. First, a systematic literature mapping and review was performed to analyze different methodologies and solutions related to the automatic generation of tailored information dashboards. The outcomes of the review led to the selection of a modeldriven approach in combination with the software product line paradigm to deal with the automatic generation of information dashboards. In this context, a meta-model was developed following a domain engineering approach. This meta-model represents the skeleton of information dashboards and data visualizations through the abstraction of their components and features and has been the backbone of the subsequent generative pipeline of these tools. The meta-model and generative pipeline have been tested through their integration in different scenarios, both theoretical and practical. Regarding the theoretical dimension of the research, the meta-model has been successfully integrated with other meta-model to support knowledge generation in learning ecosystems, and as a framework to conceptualize and instantiate information dashboards in different domains. In terms of the practical applications, the focus has been put on how to transform the meta-model into an instance adapted to a specific context, and how to finally transform this later model into code, i.e., the final, functional product. These practical scenarios involved the automatic generation of dashboards in the context of a Ph.D. Programme, the application of Artificial Intelligence algorithms in the process, and the development of a graphical instantiation platform that combines the meta-model and the generative pipeline into a visual generation system. Finally, different case studies have been conducted in the employment and employability, health, and education domains. The number of applications of the meta-model in theoretical and practical dimensions and domains is also a result itself. Every outcome associated to this thesis is driven by the dashboard meta-model, which also proves its versatility and flexibility when it comes to conceptualize, generate, and capture knowledge related to dashboards and data visualizations

How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

Challenges for engineering students working with authentic complex problems

Engineers are important participants in solving societal, environmental and technical problems. However, due to an increasing complexity in relation to these problems new interdisciplinary competences are needed in engineering. Instead of students working with monodisciplinary problems, a situation where students work with authentic complex problems in interdisciplinary teams together with a company may scaffold development of new competences. The question is: What are the challenges for students structuring the work on authentic interdisciplinary problems? This study explores a three-day event where 7 students from Aalborg University (AAU) from four different faculties and one student from University College North Denmark (UCN), (6th-10th semester), worked in two groups at a large Danish company, solving authentic complex problems. The event was structured as a Hackathon where the students for three days worked with problem identification, problem analysis and finalizing with a pitch competition presenting their findings. During the event the students had workshops to support the work and they had the opportunity to use employees from the company as facilitators. It was an extracurricular activity during the summer holiday season. The methodology used for data collection was qualitative both in terms of observations and participants’ reflection reports. The students were observed during the whole event. Findings from this part of a larger study indicated, that students experience inability to transfer and transform project competences from their previous disciplinary experiences to an interdisciplinary setting