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

Pràctica continuada i feedback automàtic en l'aprenentatge de matemàtiques en línia: un estudi des de la perspectiva de les analítiques d'aprenentatge

Aquesta tesi ha adoptat la perspectiva de les analítiques d'aprenentatge i s'ha centrat en dues assignatures de matemàtiques en línia de la UOC: Anàlisi matemàtica i Estadística. Hem observat que la qualificació a l'examen final està relacionada amb la realització de qüestionaris i la puntuació obtinguda. També hem comprovat que no presentar-se a l'examen final o no superar-lo és predictible a partir de les qualificacions obtingudes als primers qüestionaris del curs. Així, doncs, s'ha dissenyat i implementat una intervenció docent que permet als estudiants fer els qüestionaris que no havien fet en el termini previst. L'anàlisi d'aquesta intervenció ha permès determinar que ha augmentat la probabilitat de reduir el nombre d'estudiants que no es presenten a l'examen final, cosa que suposa reduir l'abandonament de l'assignatura. Aquesta tesi ens ha permès concloure que mantenir el compromís dels estudiants al llarg del curs mitjançant la realització de qüestionaris amb correcció i feedback automàtics en assignatures de matemàtiques en línia ajuda a l'assoliment dels objectius d'aprenentatge.This thesis adopts a learning analytics approach and focuses on two online mathematics courses at the Universitat Oberta de Catalunya (UOC): Calculus and Statistics. Our findings suggest that final exam scores are related to taking quizzes, as well as to quiz scores. Specifically, we show that not taking or succeeding in the final exam can be predicted from students' scores on the first few quizzes of the academic year. A teaching intervention was designed and implemented to allow students to take any of the quizzes that they had not submitted before the original deadline. Analysing the effectiveness of this intervention, we have found that it improves students' chances of taking the final exam, and therefore reduces student drop-out in the Statistics course. This doctoral thesis has allowed us to conclude that, for online mathematics courses, being engaged throughout the course by taking quizzes with automatic correction and feedback helps students achieve their learning goals.Esta tesis ha adoptado la perspectiva de las analíticas de aprendizaje y se ha centrado en dos asignaturas de matemáticas en línea de la UOC: Análisis matemático y Estadística. Hemos observado que la calificación en el examen final está relacionada con la realización de cuestionarios y la puntuación obtenida. Asimismo, hemos comprobado que no presentarse al examen final o no superarlo es predecible a partir de las calificaciones obtenidas en los primeros cuestionarios del curso. Así, se ha diseñado e implementado una intervención docente que permite a los estudiantes hacer los cuestionarios que no habían realizado en el plazo previsto. El análisis de esta intervención ha permitido determinar que ha aumentado la probabilidad de reducir el número de estudiantes que no se presentan al examen final, hecho que supone reducir el abandono de la asignatura. Esta tesis nos ha permitido concluir que mantener el compromiso de los estudiantes a lo largo del curso mediante la realización de cuestionarios con corrección y feedback automáticos en asignaturas de matemáticas en línea ayuda a alcanzar los objetivos de aprendizaje

Learning analytics summer institute Spain 2019 : Learning analytics in higher education : Preface to the conference proceeding

The seventh edition of the Learning Analytics Summer Institute Spain, LASI Spain 19 was held in Vigo on June 27th and 28th, 2019. Under the main theme of “Learning Analytics in Higher Education”, the conference was organized by Universidade de Vigo, in collaboration with the SNOLA (Spanish Network of Learning Analytics) research network and TELGalicia. LASI Spain 19 is conceived as a platform to catalyze educators, technologists, researchers, enterprise and policymakers around shaping the next generation of learning infrastructures to truly serve the needs now facing the education sector. LASI Spain 19 is part of the Learning Analytics Summer Institute locals; LASI worldwide events,sponsored by SoLAR (Society for Learning Analytics Research), are strategic events that bring the right mix of people together for an intensive ‘summer camp’ that serves as an intellectual and social springboard to accelerate the maturation of learning analytics