A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania

A gap in competencies or in capabilities?: the role of regional universities in developing scientific and technological skills in Campania The paper assesses the role of universities in resolving the STEM (Science, Technology, Engineering and Mathematics) skills gap in the Campania region of Southern Italy. The results are shown to hinge on a doubled supply/demand model, involving a first upstream stage (logically if not chronologically) of derived demands for and supplies of STEM-based skill development within universities, and a second downstream stage of the usage of these skills in industrial firms. The main objective of this work is to re-examine the role of conventional ‘knowledge capital’ arguments for the role of universities in development processes in catching-up regions of the EU – i.e. human capital and R&D capital, or what will be identified here as ‘competencies’ – as against what we refer to as ‘capabilities’ arguments, reflected here in better ways in which universities might adapt to the actual needs of industry for highly skilled workers and research outcomes. The results suggest that the STEM skills gap is not clearly a deficiency just in capabilities, but more so in the links between capabilities and competencies. Moreover, the STEM universities are trying to feed the interaction with industry, however it is still left mostly to the personal relationships of the professors or their administrative counterparts, e.g. head of the T&T office, and/or to placement. Key words: Derived demand and supply, STEM subjects, Mezzogiorno region, skills gap, competencies and capabilities.

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Motivational Factors in the Use of Videoconferences to Carry out Tutorials in Spanish Universities in the Post-Pandemic Period

Many of the tools used for virtual teaching during the pandemic had not been used previously, but they could continue to be used when traditional teaching returns. For this reason, this study focused on locating the key motivational factors for the possible continuation of the use of one of these tools, videoconferencing, to carry out tutorials in Spanish universities as a complement to face-to-face tutorials. For this, a literary review was conducted to obtain a list of motivational factors that may influence teachers to continuing using it, and a causal study was performed with university professors (through fuzzy cognitive maps) to identify the causal relationships among these factors and classify them by their relevance in making a decision. The most influential factors are intention, attitude and perceived compatibility with how tutorials are given, and the negative factors include quality management and trus

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

Communities of practice in academia

Up to now, the relationships among the fundamental notions of communities of practice (CoPs), i.e. knowledge, participation, identity, and artefact development have been based mainly on results from qualitative studies; they are not yet sufficiently based on quantitative evidence. Starting from a literature review, we formulate a quantitative, causal model of CoPs that describes these variables in the context of academic communities, and aim to validate this model in two academic CoPs with a total of N = 208 participants. A cluster analysis classifies the participants into clusters that are in line with the core-periphery structure known from previous qualitative studies. A regression analysis provides evidence for the hypothesized model on the basis of quantitative data. Suggested directions for future research are to focus on factors that determine CoP participants’ contributions to artefact development and on approaches to automated monitoring of virtual CoPs