106 research outputs found

    Designing intelligent support for learning from and in everyday contexts

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    Motivation and engagement in learning benefit from a good match of learning settings and materials to individual learner contexts. This includes intrinsic context factors such as prior knowledge and personal interests but also extrinsic factors such as the current environment. Recent developments in adaptive and intelligent technology enable the personalisation of context-aware learning. For example, computer vision algorithms, machine translation, and Augmented Reality make it possible to support the creation of meaningful connections between learners and their context. However, for successful adoption in everyday life, these technologies also need to consider the learner experience. This thesis investigates the design of personalised context-aware learning experiences through the lens of ubiquitous and self-directed language learning as a multi-faceted learning domain. Specifically, it presents and discusses the design, implementation, and evaluation of technology support for learning in and from learners’ everyday contexts with a strong focus on the learner perspective and user experience. The work is guided by four different roles that technology can take on in context-aware ubiquitous learning: For enhancing learning situations, it can (1) sense and (2) trigger in learners’ everyday contexts. For enhancing learning contents, it can (3) augment activities and (4) generate learning material from learner everyday contexts. With regards to the sensing role, the thesis investigates how learners typically use mobile learning apps in everyday contexts. Activity and context logging, combined with experience sampling, confirm that mobile learning sessions spread across the day and occur in different settings. However, they are typically short and frequently interrupted. This indicates that learners may benefit from better integrating learning into everyday contexts, e.g. by supporting task resumption. Subsequently, we explore how this integration could be supported with intelligent triggers linked to opportune moments for learning. We conceptualise and evaluate different trigger types based on interaction patterns and context detection. Our findings show that simple interactions (e.g. plugging in headphones) are promising for capturing both availability and willingness to engage in a learning activity. We discuss how similar interaction triggers could be adapted to match individual habits. In the area of enhancing learning contents, we first investigate how enjoyable everyday activities could be augmented for learning without disrupting these activities. Specifically, we assess the learner experience with interactive grammar support in e-readers and adapted captions for audio-visual media. Participants in our studies felt that the learning augmentations successfully supported their learning process. The information load of the learning support should match the learners’ current needs to maintain the activity flow. Learners may need encouragement to opt for novel concepts optimised for learning (e.g. time-synchronised captions) rather than sticking to habits (e.g. standard captions). Next, the thesis explores learner needs and preferences in generating their own personalised learning material from their context. We design and evaluate automated content generation methods that generate learning opportunities from objects in the learner’s environment. The connection to the learner’s context is established with state-of-the-art technology, such as object detection and Augmented Reality. Through several user studies, we show that learning performance and engagement with auto-generated personalised learning material is comparable to predefined and manually generated content. Findings further indicate that the success of personalisation depends on the effort required to generate content and whether the generation results match the learner’s expectations. Through the different perspectives examined in this thesis, we provide new insights into challenges and opportunities that we synthesise in a framework for context-aware ubiquitous learning technology. The findings also have more general implications for the interaction design of personalised and context-aware intelligent systems. Notably, for the auto-generation of personalised content, it is essential to consider not only correctness from a technological perspective but also how users may perceive the results.Lernmotivation und Engagement profitieren davon, wenn Lernumgebungen und Lernmaterialien auf den individuellen Kontext der Lernenden abgestimmt sind. Dieser umfasst sowohl intrinsische Faktoren wie Vorkenntnisse und persönliche Interessen, aber auch extrinsische Faktoren wie die aktuelle Umgebung. Aktuelle Weiterentwicklungen im Bereich adaptiver und intelligenter Technologien ermöglichen es, Lernen kontextbewusst zu personalisieren. So können mithilfe von Computer-Vision-Algorithmen, maschineller Übersetzung und Augmented Reality sinnvolle Verknüpfungen zwischen Lernenden und ihrem Kontext geschaffen werden. Allerdings müssen diese Technologien für einen erfolgreichen Einsatz im Alltag auch die Lernerfahrung mit einbeziehen. Diese Arbeit untersucht die Gestaltung personalisierter kontextbewusster Lernerfahrungen aus der Perspektive des ubiquitären und self-directed Learning im Sprachenlernen, einem vielseitigen Lernbereich. Insbesondere wird die Konzeption, Implementierung und Evaluierung von Technologieunterstützung für das Sprachenlernen in und aus dem Alltagskontext der Lernenden vorgestellt und diskutiert, wobei der Schwerpunkt auf der Perspektive der Lernenden und der Nutzererfahrung liegt. Die Arbeit orientiert sich an vier verschiedenen Rollen, die Technologie im kontextbewussten Lernen einnehmen kann. Um Lernsituationen anzureichern, kann Technologie im Alltagskontext von Lernenden (1) erfassen und (2) auslösen. Um Lerninhalte anzureichern, kann Technologie aus dem Alltagskontext (3) Aktivitäten augmentieren und (4) Inhalte generieren. Im Hinblick auf die erfassende Rolle von Technologie wird in dieser Arbeit untersucht, wie die Lernenden mobile Lern-Apps in alltäglichen Kontexten nutzen. Die Aufzeichnung von Aktivitäten und Kontexten in Kombination mit Experience Sampling bestätigt, dass Lerneinheiten im mobilen Lernen über den Tag verteilt sind und in verschiedenen Umgebungen stattfinden. Allerdings sind sie in der Regel kurz und werden häufig unterbrochen. Dies deutet darauf hin, dass die Lernenden von einer besseren Integration des Lernens in ihren Alltagskontext profitieren könnten, z. B. durch Unterstützung des Wiedereinstiegs nach einer Unterbrechung. Anschließend untersuchen wir, wie diese Integration durch intelligente Trigger unterstützt werden könnte, die mit passenden Lernzeitpunkten verknüpft sind. Wir konzipieren und evaluieren verschiedene Arten von Triggern auf Basis von Interaktionsmustern und Kontexterkennung. Unsere Ergebnisse zeigen, dass einfache Interaktionen (z. B. das Einstecken von Kopfhörern) vielversprechend dafür sind, sowohl die Verfügbarkeit als auch die Bereitschaft für eine Lernaktivität zu erfassen. Wir diskutieren, wie ähnliche Interaktionstrigger an individuelle Gewohnheiten angepasst werden können. Im Bereich der Augmentierung von Lerninhalten untersuchen wir zunächst, wie unterhaltsame Alltagsaktivitäten für das Lernen aufbereitet werden können, ohne diese Aktivitäten zu beeinträchtigen. Konkret bewerten wir die Lernerfahrung mit interaktiver Grammatikunterstützung in E-Readern und angepassten Untertiteln für audiovisuelle Medien. Die Teilnehmer:innen unserer Studien fanden, dass die Lernunterstützung ihren Lernprozess erfolgreich förderte. Die Informationslast im Lernsystem sollte auf die aktuellen Bedürfnisse der Lernenden angepasst werden, damit das Flow-Erlebnis nicht beeinträchtigt wird. Die Lernenden brauchen möglicherweise Ermutigung dafür, sich für neuartige, lernoptimierte Konzepte zu entscheiden (z. B. zeitsynchrone Untertitel), anstatt an Gewohnheiten festzuhalten (z. B. Standarduntertitel). Als Nächstes werden in dieser Arbeit die Bedürfnisse und Präferenzen der Lernenden bei der Erstellung ihres eigenen personalisierten Lernmaterials aus ihrem Kontext untersucht. Insbesondere werden Methoden zur automatischen Generierung von Inhalten entwickelt und evaluiert, die Lernmöglichkeiten aus Objekten in der Umgebung des Lernenden generieren. Die Verbindung zum Kontext des Lernenden wird durch aktuelle Technologien wie Objekterkennung und Augmented Reality hergestellt. Wir zeigen anhand mehrerer Nutzerstudien, dass die Lernleistung und das Engagement bei automatisch personalisiertem Lernmaterial mit vordefinierten und manuell erstellten Inhalten vergleichbar sind. Die Ergebnisse zeigen außerdem, dass der Erfolg der Personalisierung vom Aufwand abhängt, der für die Erstellung der Inhalte erforderlich ist, und davon, ob die generierten Materialien den Erwartungen der Lernenden entsprechen. Die verschiedenen Perspektiven, die in dieser Arbeit untersucht werden, bieten neue Einblicke in Herausforderungen und Möglichkeiten, die wir in einem Framework für kontextbewusste ubiquitäre Lerntechnologie zusammenfassen. Die Ergebnisse haben auch allgemeinere Auswirkungen auf die Gestaltung der Interaktion mit personalisierten und kontextbewussten intelligenten Systemen. Beispielsweise ist es bei der automatischen Generierung personalisierter Inhalte wichtig, nicht nur die Korrektheit aus technologischer Sicht zu berücksichtigen, sondern auch, wie die Nutzer die Ergebnisse wahrnehmen

    Augmented reality potential and hype: towards an evaluative framework

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    Augmented Reality adds a layer of digital information to a live direct or indirect view of a real-world environment. Of late, many claims have been made about the potential of augmented reality software in education. Technically such software may offer many exciting features but little research has been done into the teaching and learning foundations upon which it is built. This is problematic because in a time of budget cuts, on the one hand, and ever increasing examples of such software, on the other, educators do not have available to them an objective framework that they can use to evaluate the potential pedagogical usefulness of such software. Furthermore, technical developers have little guidance as to the pedagogical expectations of educators. By focusing on the area of foreign language teaching this article takes a first step towards addressing this research gap by proposing an evaluative framework that has been constructed with reference to teaching and learning scholarship, as opposed to that of digital humanities or computer science. It tests this framework using a series of case studies dealing with existing augmented reality applications for language teaching and learning and those which could be repurposed. It concludes that the evaluative framework created in this study has established a potentially useful baseline for making decisions about the possible use of augmented reality applications for teaching and learning in the classroom. We hold that the integration of such a framework with existing digital humanities and computer science methods of evaluation may result in a more objective and interdisciplinary framework that can be used for the evaluation of such software

    Will mobile learning change language learning?

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    The use of mobile phones and other portable devices is beginning to have an impact on how learning takes place in many disciplines and contexts, including language learning. Learners who are not dependent on access to fixed computers can engage in activities that relate more closely to their current surroundings, sometimes crossing the border between formal and informal learning. This creates the potential for significant change in teaching and learning practices. Taking the broader field of mobile learning as the setting within which developments in mobile-assisted language learning may be understood, the paper argues that an emphasis on mobility can lead to new perspectives and practices. The paper offers reflections on what mobile learning has to offer and considers whether it is likely to change how languages are taught and learnt. ‘Mobile learning’ is not a stable concept; therefore its current interpretations need to be made explicit. Examples of current projects and practices show an affinity between mobile and games-based learning, and can further illuminate what is distinctive and worthwhile about mobile learning

    Cognition-aware systems to support information intake and learning

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    Knowledge is created at an ever-increasing pace putting us under constant pressure to consume and acquire new information. Information gain and learning, however, require time and mental resources. While the proliferation of ubiquitous computing devices, such as smartphones, enables us to consume information anytime and anywhere, technologies are often disruptive rather than sensitive to the current user context. While people exhibit different levels of concentration and cognitive capacity throughout the day, applications rarely take these performance variations into account and often overburden their users with information or fail to stimulate. This work investigates how technology can be used to help people effectively deal with information intake and learning tasks through cognitive context-awareness. By harvesting sensor and usage data from mobile devices, we obtain people's levels of attentiveness, receptiveness, and cognitive performance. We subsequently use this cognition-awareness in applications to help users process information more effectively. Through a series of lab studies, online surveys, and field experiments we follow six research questions to investigate how to build cognition-aware systems. Awareness of user's variations in levels of attention, receptiveness, and cognitive performance allows systems to trigger appropriate content suggestions, manage user interruptions, and adapt User Interfaces in real-time to match tasks to the user's cognitive capacities. The tools, insights, and concepts described in this book allow researchers and application designers to build systems with an awareness of momentary user states and general circadian rhythms of alertness and cognitive performance

    On designing a pervasive mobile learning platform

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    Hacia un método de predicción de resultados de evaluación en un contexto de micro aprendizaje

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    Este artículo presenta un método para predecir los resultados de la evaluación de los alumnos que interactúan con un sistema de microaprendizaje consciente del contexto. Usamos ASUM-DM para guiar diferentes tareas de análisis de datos, incluida la aplicación de un algoritmo genético que selecciona las características de mayor peso de la predicción. Luego, aplicamos modelos de aprendizaje automático como Random Forest, Gradient Boosting Tree, Decision Tree, SVM y Neural Networks para entrenar datos y evaluar los efectos del contexto, ya sea el éxito o el fracaso de la evaluación del alumno. Estamos interesados ​​en encontrar el modelo de influencia significativa del contexto en los resultados de la evaluación del alumno. El modelo Random Forest proporcionó una precisión del 94%, que se calculó con la técnica de validación cruzada. Por tanto, es posible concluir que el modelo puede predecir con precisión el resultado de la evaluación y relacionarlo con el contexto del alumno. El resultado del modelo es una idea útil para enviar notificaciones a los alumnos para mejorar el proceso de aprendizaje. Queremos ofrecer recomendaciones sobre el comportamiento y el contexto del alumno y adaptar el contenido de microaprendizaje en el futuro.This paper presents a method for predicting the evaluation results of learners interacting with a context-aware microlearning system. We use ASUM-DM to guide di erent data analytics tasks, including applying a genetic algorithm that selects the prediction's highest weight features. Then, we apply machine learning models like Random Forest, Gradient Boosting Tree, Decision Tree, SVM, and Neural Networks to train data and evaluate the context's e ects, either success or failure of the learner's evaluation. We are interested in nding the model of signi cant context-in uence to the learner's evaluation results. The Random Forest model provided an accuracy of 94%, which was calculated with the cross-validation technique. Thus, it is possible to conclude that the model can accurately predict the evaluation result and relate it with the learner context. The model result is a useful insight for sending noti cations to the learners to improve the learning process. We want to provide recommendations about learner behavior and context and adapt the microlearning content in the future

    GEOLINDO: Aplicación móvil para el aprendizaje de idiomas basado en la localización del usuario

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    Treball final de Màster Universitari en Sistemes Intel.ligents (Pla de 2013). Codi: SIE043. Curs acadèmic 2017-2018Los objetivos a llevar a cabo con este proyecto se listan a continuación: 1. Estudiar las nuevas técnicas de aprendizaje en el sector educativo a través de dispositivos móviles mostrando especial interés en la tecnología utilizada para tener en cuenta la localización del usuario. 2. Desarrollar una aplicación como prueba de concepto a. Con la que se aprenda vocabulario relacionado con la localización del usuario. b. Que se ajuste a las necesidades y la capacidad de aprendizaje del usuario, enseñando palabras nuevas y afianzando las que ya sabe. c. Que evalúe el aprendizaje del usuario. 3. Comparar los efectos en el aprendizaje del usuario por la aplicación utilizando la conciencia del contexto actual del usuario y la misma aplicación utilizando el aprendizaje libre (sin tener en cuenta la ubicación)

    WaitSuite: Productive Use of Diverse Waiting Moments

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    The busyness of daily life makes it difficult to find time for informal learning. Yet, learning requires significant time and effort, with repeated exposures to educational content on a recurring basis. Despite the struggle to find time, there are numerous moments in a day that are typically wasted due to waiting, such as while waiting for the elevator to arrive, wifi to connect, or an instant message to arrive. We introduce the concept of wait-learning: automatically detecting wait time and inviting people to learn while waiting. Our approach is to design seamless interactions that augment existing wait time with productive opportunities. Combining wait time with productive work opens up a new class of software systems that overcome the problem of limited time. In this article, we establish a design space for wait-learning and explore this design space by creating WaitSuite, a suite of five different wait-learning apps that each uses a different kind of waiting. For one of these apps, we conducted a feasibility study to evaluate learning and to understand how exercises should be timed during waiting periods. Subsequently, we evaluated multiple kinds of wait-learning in a two-week field study of WaitSuite with 25 people. We present design implications for wait-learning, and a theoretical framework that describes how wait time, ease of accessing the learning task, and competing demands impact the effectiveness of wait-learning in different waiting scenarios. These findings provide insight into how wait-learning can be designed to minimize interruption to ongoing tasks and maximize engagement with learning
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