Constructing a user experience user experience-based mobile learning environment

Mobile learning, in the past defined as learning with mobile devices, now refers to any type of learning-on-the-go or learning that takes advantage of mobile technologies. This new definition shifted its focus from the mobility of technology to the mobility of the learner (O'Malley and Stanton 2002; Sharples, Arnedillo-Sanchez et al. 2009). Placing emphasis on the mobile learner’s perspective requires studying “how the mobility of learners augmented by personal and public technology can contribute to the process of gaining new knowledge, skills, and experience” (Sharples, Arnedillo-Sanchez et al. 2009). The demands of an increasingly knowledge based society and the advances in mobile phone technology are combining to spur the growth of mobile learning. Around the world, mobile learning is predicted to be the future of online learning, and is slowly entering the mainstream education. However, for mobile learning to attain its full potential, it is essential to develop more advanced technologies that are tailored to the needs of this new learning environment. A research field that allows putting the development of such technologies onto a solid basis is user experience design, which addresses how to improve usability and therefore user acceptance of a system. Although there is no consensus definition of user experience, simply stated it focuses on how a person feels about using a product, system or service. It is generally agreed that user experience adds subjective attributes and social aspects to a space that has previously concerned itself mainly with ease-of-use. In addition, it can include users’ perceptions of usability and system efficiency. Recent advances in mobile and ubiquitous computing technologies further underline the importance of human-computer interaction and user experience (feelings, motivations, and values) with a system. Today, there are plenty of reports on the limitations of mobile technologies for learning (e.g., small screen size, slow connection), but there is a lack of research on user experience with mobile technologies. This dissertation will fill in this gap by a new approach in building a user experience-based mobile learning environment. The optimized user experience we suggest integrates three priorities, namely a) content, by improving the quality of delivered learning materials, b) the teaching and learning process, by enabling live and synchronous learning, and c) the learners themselves, by enabling a timely detection of their emotional state during mobile learning. In detail, the contributions of this thesis are as follows: • A video codec optimized for screencast videos which achieves an unprecedented compression rate while maintaining a very high video quality, and a novel UI layout for video lectures, which together enable truly mobile access to live lectures. • A new approach in HTTP-based multimedia delivery that exploits the characteristics of live lectures in a mobile context and enables a significantly improved user experience for mobile live lectures. • A non-invasive affective learning model based on multi-modal emotion detection with very high recognition rates, which enables real-time emotion detection and subsequent adaption of the learning environment on mobile devices. The technology resulting from the research presented in this thesis is in daily use at the School of Continuing Education of Shanghai Jiaotong University (SOCE), a blended-learning institution with 35.000 students.

Learning through life

Web 2.0 und soziale Netzwerke gaben erste Impulse für neue Formen der Online-Lehre, welche die umfassende Vernetzung von Objekten und Nutzern im Internet nachhaltig einsetzen. Die Vielfältigkeit der unterschiedlichen Systeme erschwert aber deren ganzheitliche Nutzung in einem umfassenden Lernszenario, das den Anforderungen der modernen Informationsgesellschaft genügt. In diesem Beitrag wird eine auf dem Konnektivismus basierende Plattform für die Online-Lehre namens “Wiki-Learnia” präsentiert, welche alle wesentlichen Abschnitte des lebenslangen Lernens abbildet. Unter Einsatz zeitgemäßer Technologien werden nicht nur Nutzer untereinander verbunden, sondern auch Nutzer mit dedizierten Inhalten sowie ggf. zugehörigen Autoren und/oder Tutoren verknüpft. Für ersteres werden verschiedene Kommunikations-Werkzeuge des Web 2.0 (soziale Netzwerke, Chats, Foren etc.) eingesetzt. Letzteres fußt auf dem sogenannten “Learning-Hub”-Ansatz, welcher mit Hilfe von Web-3.0-Mechanismen insbesondere durch eine semantische Metasuchmaschine instrumentiert wird. Zum Aufzeigen der praktischen Relevanz des Ansatzes wird das mediengestützte Juniorstudium der Universität Rostock vorgestellt, ein Projekt, das Schüler der Abiturstufe aufs Studium vorbereitet. Anhand der speziellen Anforderungen dieses Vorhabens werden der enorme Funktionsumfang und die große Flexibilität von Wiki-Learnia demonstriert.

Rahmenwerk für zielgruppenorientiertes Blended E-Learning im MINT-Bereich im Kontext des Lebenslangen Lernens

Das heutige Leben der Menschen ist vom Internet durchdrungen, kaum etwas ist nicht „vernetzt“ oder „elektronisch verfügbar“. Die Welt befindet sich im Wandel, die „Informationsgesellschaft“ konsumiert in Echtzeit Informationen auf mobilen Endgeräten, unabhängig von Zeit und Ort. Dies gilt teilweise auch für den Aus- und Weiterbildungssektor: Unter „E-Learning“ versteht man die elektronische Unterstützung des Lernens. Gelernt wird „online“; Inhalte sind digital verfügbar. Zudem hat sich die Lebenssituation der sogenannten „Digital Natives“, der jungen Individuen in der Informationsgesellschaft, verändert. Sie fordern zeitlich und räumlich flexible Ausbildungssysteme, erwarten von Bildungsinstitutionen umfassende digitale Verfügbarkeit von Informationen und möchten ihr Leben nicht mehr Lehr- und Zeitplänen unterordnen – das Lernen soll zum eigenen Leben passen, lebensbegleitend stattfinden. Neue „Lernszenarien“, z.B. für alleinerziehende Teilzeitstudierende oder Berufstätige, sollen problemlos möglich werden. Dies soll ein von der europäischen Union erarbeitetes Paradigma leisten, das unter dem Terminus „Lebenslanges Lernen“ zusammengefasst ist. Sowohl E-Learning, als auch Lebenslanges Lernen gewinnen an Bedeutung, denn die (deutsche) Wirtschaft thematisiert den „Fachkräftemangel“. Die Nachfrage nach speziell ausgebildeten Ingenieuren im MINT-Bereich soll schnellstmöglich befriedigt, die „Mitarbeiterlücke“ geschlossen werden, um so weiterhin das Wachstum und den Wohlstand zu sichern. Spezielle E-Learning-Lösungen für den MINT-Bereich haben das Potential, eine schnelle sowie flexible Aus- und Weiterbildung für Ingenieure zu bieten, in der Fachwissen bezogen auf konkrete Anforderungen der Industrie vermittelt wird. Momentan gibt es solche Systeme allerdings noch nicht. Wie sehen die Anforderungen im MINT-Bereich an eine solche E-Learning-Anwendung aus? Sie muss neben neuen Technologien vor allem den funktionalen Anforderungen des MINTBereichs, den verschiedenen Zielgruppen (wie z.B. Bildungsinstitutionen, Lerner oder „Digital Natives“, Industrie) und dem Paradigma des Lebenslangen Lernens gerecht werden, d.h. technische und konzeptuelle Anforderungen zusammenführen. Vor diesem Hintergrund legt die vorliegende Arbeit ein Rahmenwerk für die Erstellung einer solchen Lösung vor. Die praktischen Ergebnisse beruhen auf dem Blended E-Learning-System des Projekts „Technische Informatik Online“ (VHN-TIO).

Governing individual learning in the transition phase of software maintenance offshoring: a dynamic perspective

Prior studies suggest that clients need to actively govern knowledge transfer to vendor staff in offshore outsourcing. In this paper, we analyze longitudinal data from four software maintenance offshore out-sourcing projects to explore why governance may be needed for knowledge transfer and how governance and the individual learning of vendor engineers inter-act over time. Our results suggest that self-control is central to learning, but may be hampered by low levels of trust and expertise at the outset of projects. For these foundations to develop, clients initially need to exert high amounts of formal and clan controls to enforce learning activities against barriers to knowledge sharing. Once learning activities occur, trust and expertise increase and control portfolios may show greater emphases on self-control.

Agrarian learning processes in Switzerland 1918–1939

This paper examines the social impacts of weather extremes and the processes of social and communicative learning a society undertakes to find alternative ways to deal with the consequences of a crisis. In the beginning of the 20th Century hunger seemed to be expelled from Europe. Switzerland – like many other European countries – was involved in a global interdependent trade system, which provided necessary goods. But at the end of World War I very cold and wet summers in 1916/17 (causing crop failure) and the difficulties in war-trade led to malnutrition and enormous price risings of general living-standards in Switzerland, which shocked the people and caused revolutionary uprisings in 1918. The experience of malnutrition during the last two years of war made clear that the traditional ways of food supply in Switzerland lacked crisis stability. Therefore various agents in the field of food production, distribution and consumption searched for alternative ways of food supply. In that sense politicians, industrialists, consumer-groups, left-wing communitarians and farmers developed several strategies for new ways in food production. Traditionally there were political conflicts in Switzerland between farmers and consumers regarding price policies, which led mainly to the conflict in 1918. Consumers accused famers of holding back food to control extortionate prices while the farmers pointed to the bad harvest causing the price rising. The collaboration of these groups in search for new forms of food-stability made social integration possible again. In addition to other crisis-factors, weather extremes can have disastrous impacts and destroy a society’s self-confidence to its core. But even such crisis can lead to processes of substantial learning that allows a regeneration of confidence and show positive influence on political stabilization. The paper focuses on the process of learning and the alternative methods of food production that were suggested by various agents working in the field during the Interwar period. To achieve that goal documents of the various associations are analyzed and newspapers have been taken into consideration. Through the method of discourse-analysis of food-production during the Interwar period, possible solutions that crossed the minds of the agents should be brought to light.

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

Storing and recalling spiking sequences is a general problem the brain needs to solve. It is, however, unclear what type of biologically plausible learning rule is suited to learn a wide class of spatiotemporal activity patterns in a robust way. Here we consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper bound on the Kullback–Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with spike-timing dependent plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. Furthermore, the learning rule for synapses that target visible neurons can be matched to the recently proposed voltage-triplet rule. The learning rule for synapses that target hidden neurons is modulated by a global factor, which shares properties with astrocytes and gives rise to testable predictions.

Implicit task sequence learning

Implicit task sequence learning (TSL) can be considered as an extension of implicit sequence learning which is typically tested with the classical serial reaction time task (SRTT). By design, in the SRTT there is a correlation between the sequence of stimuli to which participants must attend and the sequence of motor movements/key presses with which participants must respond. The TSL paradigm allows to disentangle this correlation and to separately manipulate the presences/absence of a sequence of tasks, a sequence of responses, and even other streams of information such as stimulus locations or stimulus-response mappings. Here I review the state of TSL research which seems to point at the critical role of the presence of correlated streams of information in implicit sequence learning. On a more general level, I propose that beyond correlated streams of information, a simple statistical learning mechanism may also be involved in implicit sequence learning, and that the relative contribution of these two explanations differ according to task requirements. With this differentiation, conflicting results can be integrated into a coherent framework.

"Too Cool to Smoke": Using Puppets to Make Learning Fun

Introduction: According to the American Cancer Society, each day, more than 4,000 teens try cigarettes for the first time, and another 2,000 become daily smokers. One-half of these teens eventually will die from a smoking-related disease. [See PDF for complete abstract]

Learning curve for robotic-assisted laparoscopic colorectal surgery.

BACKGROUND: Robotic-assisted laparoscopic surgery (RALS) is evolving as an important surgical approach in the field of colorectal surgery. We aimed to evaluate the learning curve for RALS procedures involving resections of the rectum and rectosigmoid. METHODS: A series of 50 consecutive RALS procedures were performed between August 2008 and September 2009. Data were entered into a retrospective database and later abstracted for analysis. The surgical procedures included abdominoperineal resection (APR), anterior rectosigmoidectomy (AR), low anterior resection (LAR), and rectopexy (RP). Demographic data and intraoperative parameters including docking time (DT), surgeon console time (SCT), and total operative time (OT) were analyzed. The learning curve was evaluated using the cumulative sum (CUSUM) method. RESULTS: The procedures performed for 50 patients (54% male) included 25 AR (50%), 15 LAR (30%), 6 APR (12%), and 4 RP (8%). The mean age of the patients was 54.4 years, the mean BMI was 27.8 kg/m(2), and the median American Society of Anesthesiologists (ASA) classification was 2. The series had a mean DT of 14 min, a mean SCT of 115.1 min, and a mean OT of 246.1 min. The DT and SCT accounted for 6.3% and 46.8% of the OT, respectively. The SCT learning curve was analyzed. The CUSUM(SCT) learning curve was best modeled as a parabola, with equation CUSUM(SCT) in minutes equal to 0.73 × case number(2) - 31.54 × case number - 107.72 (R = 0.93). The learning curve consisted of three unique phases: phase 1 (the initial 15 cases), phase 2 (the middle 10 cases), and phase 3 (the subsequent cases). Phase 1 represented the initial learning curve, which spanned 15 cases. The phase 2 plateau represented increased competence with the robotic technology. Phase 3 was achieved after 25 cases and represented the mastery phase in which more challenging cases were managed. CONCLUSIONS: The three phases identified with CUSUM analysis of surgeon console time represented characteristic stages of the learning curve for robotic colorectal procedures. The data suggest that the learning phase was achieved after 15 to 25 cases.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Sustainable Wastewater Management: Is it Possible to Regulate Micropollution in the Future by Learning from the Past? A Policy Analysis

This paper applies a policy analysis approach to the question of how to effectively regulate micropollution in a sustainable manner. Micropollution is a complex policy problem characterized by a huge number and diversity of chemical substances, as well as various entry paths into the aquatic environment. It challenges traditional water quality management by calling for new technologies in wastewater treatment and behavioral changes in industry, agriculture and civil society. In light of such challenges, the question arises as to how to regulate such a complex phenomenon to ensure water quality is maintained in the future? What can we learn from past experiences in water quality regulation? To answer these questions, policy analysis strongly focuses on the design and choice of policy instruments and the mix of such measures. In this paper, we review instruments commonly used in past water quality regulation. We evaluate their ability to respond to the characteristics of a more recent water quality problem, i.e., micropollution, in a sustainable way. This way, we develop a new framework that integrates both the problem dimension (i.e., causes and effects of a problem) as well as the sustainability dimension (e.g., long-term, cross-sectoral and multi-level) to assess which policy instruments are best suited to regulate micropollution. We thus conclude that sustainability criteria help to identify an appropriate instrument mix of end-of-pipe and source-directed measures to reduce aquatic micropollution.

Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.