Society is become increasingly reliant on data, making it necessary to ensure that all citizens are equipped with the skills needed to be data literate. We argue that the foundations for a data literate society begin by acquiring key data literacy competences in school. However, as yet there is no clear definition of what these should be. This paper explores the different perspectives currently offered on both data and statistical literacy and then critically examines to what extent these address the data literacy needs of citizens in today’s society. We survey existing approaches to teaching data literacy in schools, to identify how data literacy is interpreted in practice. Based on these analyses, we propose a definition of data literacy that is focused on using data to understand real world phenomena. The contribution of this paper is the creation of a common foundation for teaching and learning data literacy skills.

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.

Internet of Things (IoT) refers to the interconnection of physical objects via the Internet. It utilises complex back-end systems which need different capabilities depending on the requirements of the system. IoT has already been used in various applications, such as agriculture, smart home, health, automobiles, and smart grids. There are many IoT platforms, each of them capable of providing specific services for such applications. Finding the best match between application and platform is, however, a hard task as it can difficult to understand the implications of small differences between platforms. This paper builds on previous work that has identified twenty-one important factors of an IoT platform, which were verified by Delphi method. We demonstrate here how these factors can be used to discriminate between five well known IoT platforms, which are arbitrarily chosen based on their market share. These results illustrate how the proposed approach provides an objective methodology that can be used to select the most suitable IoT platform for different business applications based on their particular requirements.

This study examines motivations, definitions, methods and challenges of evaluating the social impacts of smart city technologies and services. It outlines concepts of social impact assessment and discusses how social impact has been included in smart city evaluation frameworks. Thematic analysis is used to investigate how social impact is addressed in eight smart city projects that prioritise human-centred design across a variety of contexts and development phases, from design research and prototyping to completed and speculative projects. These projects are notable for their emphasis on human, organisational and natural stakeholders; inclusion, participation and empowerment; new methods of citizen engagement; and relationships between sustainability and social impact. At the same time, there are gaps in the evaluation of social impact in both the smart city indexes and the eight projects. Based on our analysis, we contend that more coherent, consistent and analytical approaches are needed to build narratives of change and to comprehend impacts before, during and after smart city projects. We propose criteria for social impact evaluation in smart cities and identify new directions for research. This is of interest for smart city developers, researchers, funders and policymakers establishing protocols and frameworks for evaluation, particularly as smart city concepts and complex technologies evolve in the context of equitable and sustainable development.