Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Community-based Data Gathering and Co-management of Marine Resources in Timor-Leste

This the final technical report regarding communication products and outputs created as a result of lessons learned from eleven years of the Fisheries Management Science Programme (FMSP). These lessons, together with tools, methods and informative experiences have been brought together into accessible communications products that aim to highlight the FMSP experiences in relation to fisheries co-management and lead the reader towards the more detailed products available. As such the project has not aimed to generate any particular new insights into any aspect of the co-management process but instead to communicate what exists to a range of stakeholders. The project has developed a communication strategy that has identified a range of target communications stakeholders including policy makers, implementing agencies and agencies with a capacity building remit who might benefit from the lessons learned. The communications strategy was developed together with two other projects to ensure a coordinated approach to the promotion of products relating to co-management and a single communications database was established through which the strategy could be implemented. Based on lessons learned in earlier uptake promotions projects, a range of communications products were developed

Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Perspectives on Deepening Teachers’ Mathematics Content Knowledge: The Case of the Oregon Mathematics Leadership Institute

The Oregon Mathematics Leadership Institute (OMLI) project served 180 Oregon teachers, and 90 administrators, across the K-12 grades from ten partner districts. OMLI offered a residential, three-week summer institute. Over the course of three consecutive summers, teachers were immersed in a total of six mathematics content classes– Algebra, Data & Chance, Discrete Mathematics, Geometry, Measurement & Change, and Number & Operations—along with an annual collegial leadership course. Each content class was designed and taught by a team of expert faculty from universities, community colleges, and K-12 districts. Each team chose a few “big ideas” on which to focus the course. For example, the Algebra team focused on algebraic structure and properties of the concept of a group, while the Data & Chance team centered their activities on the exploration of ideas of central tendency and variation using statistics and data analysis software packages. The content in all of the courses was addressed through deep investigation of the mathematics of tasks that had been selected and adapted from resources for K-12 mathematics classrooms. In addition to mathematics content, the courses were designed with specific attention to socio-mathematical norms, issues of status differences among learners, and the selection and implementation of group-worthy tasks for group work. The faculty attended sessions grounded in the work of Elizabeth Cohen on strategies for working with heterogeneous groups of learners (Cohen, 1994; Cohen et al, 1999) which was central to the OMLI design and implementation. Institute faculty modeled these strategies in the Institute classrooms and made their moves as transparent as possible, so that the teachers would be able to grapple with these strategies during the Institute and plan for implementation in their own classrooms. The Data & Chance course also modeled uses of technology in instruction using Tinkerplots. Generalization and justification were emphasized as mathematical ways of learning and knowing, and institute faculty conducted classroom discussions that intentionally modeled pushing for generalization and justification

Student Privacy in Learning Analytics: An Information Ethics Perspective

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods

Report on proposals for the development, harmonisation and quality assurance of organic data collection and processing systems (DCPS)

This report represents the conclusion of the European seminar on development, harmonisation and quality assurance of organic data collection and processing systems (Berlin, April 2004) as well as of the first phase of the EISFOM-project. - In the first chapter the objectives and general approach of this workpackage are described. - Chapter 2 focuses on quality assurance, the main results of WP2 and WP3 and the European Seminar in Berlin (see Recke et al. 2004; https://orgprints.org/2935/. Furthermore, the strengths and weaknesses of organic DCPS (data collection and processing systems) are analysed and the chapter closes with proposals for the development of organic DCPSs. - Chapter 3 focuses on results of expert interviews on the main barriers for the implementation of improved organic statistical data collection and processing systems. - Chapter 4 gives a summary and some general conclusions are drawn. This report provides perspectives on how the above mentioned issues of the European Action Plan might be implemented

Foreword: Making Sense of Information for Environmental Protection

Despite the ubiquity of information, no one has proposed calling the present era the Knowledge Age. Knowledge depends not only on access to reliable information, but also on sound judgment regarding which information to access and how to situate that information in relation to the values and purposes that comprise the individual\u27s or the social group\u27s larger projects. This is certainly the case for wise and effective environmental governance. A regulator needs accurate information to understand the nature of a problem and the consequences of potential responses. Likewise, the regulated community needs information to decide how best to comply with adopted rules, and the public needs information in order to accept the credibility and legitimacy of the regulatory regime. But governance also requires judgment regarding how to manage information itself - how to structure burdens of proof in light of goals such as public safety or promotion of economic growth, how to balance the public\u27s interest in disclosure against competing aims such as national security or the protection of trade secrets, whether to withhold information in the belief that it may actually be harmful to the recipient, and so on. This paper, written as a foreword for the Texas Law Review\u27s symposium issue, Harnessing the Power of Information for the Next Generation of Environmental Law, provides a model to understand the role of information in environmental law - how it is generated, utilized, and disseminated within regulatory processes. Drawing on the diverse and significant insights of the symposium articles, the paper attempts both to make sense of the role of information in environmental protection and to highlight significant questions and concerns