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189 201 31 3 ; S ; Different methods can be used for learning, and they can be compared in several aspects, especially those related to learning outcomes. In this paper, we present a study in order to compare the learning effectiveness and satisfaction of children using an iPhone game for learning the water cycle vs. the traditional classroom lesson. The iPhone game includes multiple interaction forms and combined augmented reality (AR) mini-games with non-AR mini-games. The traditional classroom lesson had the same learning content as the iPhone game. Thirty-eight children participated in the study. The analyses showed that the children made significant learning gains about the water cycle, regardless of the method used. Even though the results showed that the iPhone method achieved higher knowledge results than the traditional classroom lesson, no statistically significant differences were found between the iPhone and the classroom lesson. When analysing the motivational outcomes, the results showed that the children found the iPhone game to be more satisfying than the classroom lessons. Since the iPhone game achieved similar learning results and a higher motivational effect than the classroom lesson, this suggests that games of this kind could be used as a tool in primary schools to reinforce students lessons. This work was funded by the Spanish Government, APRENDRA project (TIN2009-14319-C02). We would like to thank the following for their contributions: Noemi Rando, Encarna Torres, Sonia, Severino Gonzalez, M. Jose Vicent, Patricia Liminana, Tamara Aguilar, Alfonso Lopez, Yolanda Martinez, Enrique Daunis, M. Jose Martinez, and Eloy Hurtado for their help. The Engeba school of Valencia (Spain). The children who participated in the study. Furió Ferri, D.; Juan, M.; Segui, I.; Vivó Hernando, RA. (2015). Mobile learning vs. traditional classroom lessons: A comparative study. Journal of Computer Assisted Learning. 31(3):189-201. doi:10.1111/jcal.12071

53 70 13 1 ; StratusLab: Claudia Platform (2013). http://stratuslab.eu/doku.php/claudia ; S ; The final publication is available at Springer via http://dx.doi.org/10.1007/s10723-014-9296-5 Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds. The authors would like to thank to thank the financial support received from ...