Saving Us From Ourselves – How Can We Make The UK More Financially Resilient?

Untold time and resource has been devoted to quantifying and analysing the UK’s low level of personal saving – and recommending ways to change it for the better. Yet despite the concerted efforts of academics, policymakers, industry and others, the number of people in the UK with few or no savings remains stubbornly high. When we do decide to save, we are prone to making poor decisions about where to put our money

Student-centered learning objects to support the self-regulated learning of computer science

The most current computing curriculum guidelines focus on designing learning materials to prepare students for lifelong learning. Under the lifelong learning paradigm, students are responsible for controlling and monitoring their learning processes. This undoubtedly includes the ability to choose suitable learning materials. Correspondingly, instructional paradigms are shifting from teacher-centered to more student-centered models that require students to be self-regulated learners. On the other hand, recent trends in learning materials’ instructional design focus on moving toward the concept of Learning Object-based instructional technology. A learning object is a unit of instruction with a specific pedagogical objective that can be used and reused in different learning contexts. Designing learning objects to support students in their self-regulated learning is not an easy task due to the lack of underlying pedagogical frameworks. It is difficult to find learning objects related to students’ specific preferences and requirements. In this study, a number of learning objects are designed to support the self-regulated learning of programming languages concepts based on the theory of learning styles. Students’ interactions with these learning objects are managed using an online learning object repository. The repository helps students identify their preferred learning styles and find the relevant learning objects. The results of the evaluations of these learning objects revealed that students perceive them to be easy to use and effective in supporting their learning about different programming languages concepts

The personal equation of interaction for interface learning: Predicting the performance of visual analysis through the assessment of individual differences

The Personal Equation of Interaction (PEI) for Interface Learning is a short self-report psychometric measure which predicts reasoning outcomes of interface learning such as accurate target identification and insights garnered through and inferred from learning interaction. By predicting outcomes, we consider why some interfaces are more appropriate than others, provide a tool for intuitive interface design, and advance the pursuit and design of interface individuation. Through study designs which use comparative interfaces and simple but imperative tasks to any interface learning, such as target identification and inferential learning, we evaluate the accuracy of analysts and how it is impacted by graphical representation. By using psychometric items culled from normed trait assessment, we have created a measure which predicts accuracy and learning, called the Personal Equation of Interaction. This prediction tool can be used in a variety of ways, including as a function or equation that puts a number on the association between analyst and interface. We also use the PEI to build profiles of analyst expert cohorts and discuss how its use might impact Visual Analytics

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

Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements

CBKR+: A Conceptual Framework for Improving Corpus Based Knowledge Representation

In Corpus Based Knowledge Representation [CBKR], limited association capability, that is, no criteria in place to extract substantial associations in the corpus, and lack of support for hypothesis testing and prediction in context, restricted the application of the methodology by information specialists and data analysts. In this thesis, the researcher proposed a framework called CBKR+ to increase the expressiveness of CBKR by identifying and incorporating association criteria to allow the support of new forms of analyses related to hypothesis testing and prediction in context. As contributions of the CBKR+ framework, the researcher (1) defined a new domain categorization model called Basis for Categorization model, (2) incorporated the Basis for Categorization model to (a) facilitate a first level categorization of the schema components in the corpus, and (b) define the Set of Criteria for Association to cover all types of associations and association agents, (3) defined analysis mechanisms to identify and extract further associations in the corpus in the form of the Set of Criteria for Association, and (4) improved the expressiveness of the representation, and made it suitable for hypothesis testing and prediction in context using the above. The application of the framework was demonstrated, first, by using it on examples from the CBKR methodology, and second, by applying it on 12 domain representations acquired from multiple sources from the physical-world domain of Criminology. The researcher arrived at the conclusion that the proposed CBKR+ framework provided an organized approach that was more expressive, and supported deeper analyses through more diagnostic and probability-based forms of queries

Holistic ICT environments for effective mathematics teaching and learning

To understand the numbers world, each child must face a path that includes propaedeutic phases and only overcoming these phases will allow the child to consolidate processes before tackling new ones. If this does not happen, the child becomes self-convinced of \u201dnot being able to understand math\u201d and becomes increasingly aware of it throughout primary school. The transition to middle school may have a significant impact on a child, with specific difficulties or disabilities in learning mathematics, especially if his/her difficulties have not yet been identified. The present thesis studies the contributions of Information and Communication Technology (ICT) in supporting various aspects of mathematical teaching and learning. A multidimensional approach was used. In the first part, empirical studies assessed the effectiveness of digital tools to identify individual differences based on cognitive profiles and emotional responses associated with math performance in children from Northern Italy. In the second part a meta-analysis and systematic review analysis were carried out to evaluate the effectiveness of interventions supporting math learning by means of ICT in the school and home environments respectively. Altogether the thesis indicates that designing holistic ICT environments proves successful for effective mathematics teaching and learning not only for typically developing children but also for students in disadvantaged situations, including those suffering from dyscalculia

Individual Differences and Instructed Second Language Acquisition: Insights from Intelligent Computer Assisted Language Learning

The present dissertation focuses on the role of cognitive individual difference factors in the acquisition of second language vocabulary in the context of intelligent computer assisted language learning (ICALL). The aim was to examine the association between working memory and declarative memory and the learning of English phrasal verbs in a web-based ICALL-mediated experiment. Following a pretest-posttest design, 127 adult learners of English were assigned to two instructional conditions, namely meaning-focused and form-focused conditions. Learners in both conditions read news texts on the web for about two weeks; learners in the form-focused condition additionally interacted with the texts via selecting multiple-choice options. The results showed that both working memory and declarative memory were predictive of vocabulary acquisition. However, only the working memory effect was modulated by the instructional context, with the effect being found exclusively in the form-focused condition, and thus suggesting the presence of an aptitude-treatment interaction. Finally, findings also revealed that learning during treatment in the form-focused group was nonlinear, and that paying attention to form and meaning simultaneously impeded global reading comprehension for intermediate, not advanced learners. From a theoretical perspective, the findings provide evidence to suggest that individual differences in both working memory and declarative memory affect the acquisition of lexical knowledge in ICALL-supported contexts. Methodologically, the current study illustrates the advantages of conducting interdisciplinary work between ICALL and second language acquisition by allowing for the collection of experimental data through a web-based, all-encompassing ICALL system. Overall, the present dissertation represents an initial attempt at characterizing who is likely to benefit from ICALL-based interventions