The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Mining Mobile Youth Cultures

In this short paper we discuss our work on coresearch devices with a young coder community, which help investigate big social data collected by mobile phones. The development was accompanied by focus groups and interviews on privacy attitudes, and aims to explore how youth cultures are tracked in mobile phone data

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes