Where is my time? Identifying productive time of lifelong learners for effective feedback services

Lifelong learners are confronted with a broad range of activities they have to manage every day. In most cases they have to combine learning, working, family life and leisure activities throughout the day. Hence, learning activities from lifelong learners are disrupted. The difficulty to find a suitable time slot to learn during the day has been identified as the most frequent cause. In this scenario mobile technologies play an important role since they can keep track of the most suitable moments to accomplish specific learning activities in context. Sampling of learning preferences on mobile devices are key benchmarks for lifelong learners to become aware on which learning task suits in which context, set realistic goals and set aside time to learn on a regular basis. The contribution of this manuscript is twofold: first, a classification framework for modelling lifelong learners’ preferences is presented based on a literature review; second, a mobile application for experience sampling is piloted aiming to identify which are the preferences from lifelong learners regarding when, how and where learning activities can be integrated. Both framework and pilot provide an important scaffold for lifelong learners to identify productive times during the day with mobile technologies.NELL

Design of a game-based pre-hospital resuscitation training for first responders

Kalz, M., Schmitz, B., Biermann, H., Klemke, R., Ternier, S., & Specht, M. (2013). Design of a game-based pre-hospital resuscitation training for first responders. In A. Holzinger, M. Ziefle, & V. Glavinić (Eds.), SouthCHI 2013, LNCS 7946 (pp. 363-372). Germany: Springer, Heidelberg.This paper reports about the design of a game-based training intervention for pre-hospital resuscitation training. Our underlying assumption is, that survival chances in cardiac arrest situations could be significantly improved, if bystanders would be better educated and prepared to help. Based on a discussion of problems of current training concepts and related educational the-ories a game-based learning intervention is proposed. The focus of the interven-tion is the improvement of procedural knowledge and self-efficacy of partici-pants. The game is designed on the basis of the ARLearn platform. The game context and game-design is discussed. Last but not least we discuss short-term and long-term evaluation scenarios.This publication was partly financed by the European Regional Development Fund (ERDF), regions of the Euregio Meuse-Rhine and the participating institutions under the INTERREG IVa program (EMR.INT4-1.2.-2011-04/070, http://www.emurgency.eu)

Design Patterns for Augmented Reality Learning Games

Augmented Reality (AR) is expected to receive a major uptake with the recent availability of high quality wearable AR devices such as Microsoft’s Hololens. However, the design of interaction with AR applications and games is still a field of experimentation and upcoming innovations in sensor technology provide new ways. With this paper, we aim to provide a step towards the structured use of design patterns for sensor-based AR games, which can also inform general application development in the field of AR

Immersive multi-user decision training games with ARLearn

Serious gaming approaches so far focus mainly on skill development, motivational aspects or providing immersive learning situations. Little work has been reported to foster awareness and decision competencies in complex deci-sion situations involving incomplete information and multiple stakeholders. We address this issue exploring the technical requirements and possibilities to de-sign games for such situations in three case studies: a hostage taking situation, a multi-stakeholder logistics case, and a health-care related emergency case. To implement the games, we use a multi-user enabled mobile game development platform (ARLearn). We describe the underlying real world situations and edu-cational challenges and analyse how these are reflected in the ARLearn games realized. Based on these cases we propose a way to increase the immersiveness of mobile learning games.SALOM

Mindergie: A pervasive learning game for pro-environmental behaviour at the workplace

This chapter reports about a pervasive learning game to increase the environmental awareness and pro-environmental behaviour at the workplace. Based on a discussion of the theoretical background and related work we introduce the game design and game elements. Results of a formative evaluation study are presented and discussed. Results show that incentive mechanisms are less important than challenging game components that involve employees in proposing solutions for energy conservation at the workplace. Conclusions are drawn for future games and energy conservation activities at the workplace.This project has been partially funded by a SURFnet innovation grant for sustainable ICT solutions and partially by the Welten Institute – Research Centre for Learning, Teaching and Technology of the Open University of the Netherlands

Achieving coordinated national immunity and cholera elimination in Haiti through vaccination: a modelling study

Background: Cholera was introduced into Haiti in 2010. Since then, more than 820 000 cases and nearly 10 000 deaths have been reported. Oral cholera vaccine (OCV) is safe and effective, but has not been seen as a primary tool for cholera elimination due to a limited period of protection and constrained supplies. Regionally, epidemic cholera is contained to the island of Hispaniola, and the lowest numbers of cases since the epidemic began were reported in 2019. Hence, Haiti may represent a unique opportunity to eliminate cholera with OCV. Methods: In this modelling study, we assessed the probability of elimination, time to elimination, and percentage of cases averted with OCV campaign scenarios in Haiti through simulations from four modelling teams. For a 10-year period from January 19, 2019, to Jan 13, 2029, we compared a no vaccination scenario with five OCV campaign scenarios that differed in geographical scope, coverage, and rollout duration. Teams used weekly department-level reports of suspected cholera cases from the Haiti Ministry of Public Health and Population to calibrate the models and used common vaccine-related assumptions, but other model features were determined independently. Findings: Among campaigns with the same vaccination coverage (70% fully vaccinated), the median probability of elimination after 5 years was 0–18% for no vaccination, 0–33% for 2-year campaigns focused in the two departments with the highest historical incidence, 0–72% for three-department campaigns, and 35–100% for nationwide campaigns. Two-department campaigns averted a median of 12–58% of infections, three-department campaigns averted 29–80% of infections, and national campaigns averted 58–95% of infections. Extending the national campaign to a 5-year rollout (compared to a 2-year rollout), reduced the probability of elimination to 0–95% and the proportion of cases averted to 37–86%. Interpretation: Models suggest that the probability of achieving zero transmission of Vibrio cholerae in Haiti with current methods of control is low, and that bolder action is needed to promote elimination of cholera from the region. Large-scale cholera vaccination campaigns in Haiti would offer the opportunity to synchronise nationwide immunity, providing near-term population protection while improvements to water and sanitation promote long-term cholera elimination. Funding: Bill & Melinda Gates Foundation, Global Good Fund, Institute for Disease Modeling, Swiss National Science Foundation, and US National Institutes of Health

A System for Multi-label Classification of Learning Objects

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking

Mobile inquiry-based learning with sensor-data in the school: Effects on student motivation

The paper discusses the design, implementation and evaluation of a pilot project that integrated inquiry-based learning with mobile game design and introduced mobile devices and sensors into classroom learningweSPOT Project - IST (FP7/2007-2013) under grant agreement N° 318499The project was supported by the SURFnet innovation grant for sustainable ICT solutions

Large enhancement of the sub-barrier fusion probability for a halo nucleus

The fusion-fission cross sections of the He-4 + U-238 and He-6 + U-238 systems have been measured, at Louvain-la-Neuve, for energies around and below the Coulomb barrier, using an array of Si detectors surrounding a UF4 target. The data taken with 4He are in good agreement with previous data and with the coupled channel fusion calculation performed with ECIS. The He-6 data show a regular trend with a large enhancement below the barrier which is attributed to the halo structure of the He-6 nucleus