Understanding Evidence-Based Interventions for Cross-Cultural Group Work: A Learning Analytics Perspective

As the numbers of international students worldwide continue to rise, one common challenge is how best to socially integrate diverse groups of students. Indeed, research demonstrates that many students form social and learning relationships with those from the same cultural background, despite benefits of cross-cultural communication. This lack of social cohesion negatively affects students, particularly when it comes to their perceptions of collaborative group work. However, few studies have analysed measurable student behaviours in group work, such as with learning analytics, to determine how culture and existing social networks influence measurable differences in contributions. Similarly, little is known about what evidence-based interventions lead to more equal participation between diverse students. In this research, learning analytics is combined with social network analysis to determine the role of social connections on group work participation, and highlight replicable interventions that can help promote social cohesion in diverse classrooms

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Safety in Numbers: Developing a Shared Analytics Services for Academic Libraries

Purpose It is clear that libraries consider the use of data to inform decision making a top priority in the next five years. Jisc’s considerable work on activity data has highlighted the lack of tools and services for libraries to exploit this data. The purpose of this paper is to explore the potential of a shared analytics service for UK academic libraries and introduce the Jisc Library Analytics and Metrics Project (LAMP). The project aims to help libraries effectively management collections and services as well as delivering pre-emptive indicators and ‘actionable insights’ to help identify new trends, personalise services and improve efficiencies, economies and effectiveness (student attainment and satisfaction and institutional reputation, for example) . The project builds on the Library Impact Data Project at the University of Huddersfield and the work of the Copac Activity Data and Collections Management tools. The paper will deliver a case study of the project, its progress to date, the challenges of such an approach and the implications the service has for academic libraries. Design, methodology or approach The paper will be a case study of the project and its institutional partners and early adopters work to date and explore both the technical and cultural challenges of the work as well as its implications for the role of the library within the institution and the services it provides. Specifically the case study will comprise of the following aspects: 1. A brief history of the work and the context of library analytics services in the UK (and internationally). A description of the approach adopted by the project, and the vision and goals of the project 2. Exploration of the challenges associated with the project. In particular the challenges around accessing and sharing the data, ‘warehousing’ and data infrastructure considerations and the design challenge of visualising the data sources in a useful and coherent way 3. Outline of the implications of the project and the resultant service. In particular the implications for benchmarking (within the UK and beyond), standards development for library statistics and impact (in particular the development of ISO 16439), service development, the role of the library within the wider institution and skills and expertise of librarians. Findings This paper will report on the initial findings of the project, which will run from January 2013 to October 2013. In particular it will consider the issues surfaced through the close engagement with the academic library community (through the projects community advisory and planning group) and the institutional early-adopters around data gathering and analysis. Practical implications Data accumulated in one context has the potential to inform decisions and interventions elsewhere. While there are a number of recognised and well understood use-cases for library analytics these tend to revolve around usage and collection management. Yet, the potential of a shared analytics service is in uncovering those links and indicators across diverse data sets. The paper will consider a number of practical impacts: Performance: benchmarking, student attainment, research productivity Design: fine tuning services, personalised support Trends: research landscape, student marketplace, utilisation of resources. The case study will explore these practical implications for libraries and what they mean for the future of the library within the academy. Originality and value of the proposal The paper will present a case study of a unique service that currently fills an important gap within the library analytics space. The paper will focus on the services potential to transform both the way the library works and how it is erceived by its users, as well as its role and relationship within the broader institution

Guest Editorial: Ethics and Privacy in Learning Analytics

The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper

Troping the Enemy: Metaphor, Culture, and the Big Data Black Boxes of National Security

This article considers how cultural understanding is being brought into the work of the Intelligence Advanced Research Projects Activity (IARPA), through an analysis of its Metaphor program. It examines the type of social science underwriting this program, unpacks implications of the agency’s conception of metaphor for understanding so-called cultures of interest, and compares IARPA’s to competing accounts of how metaphor works to create cultural meaning. The article highlights some risks posed by key deficits in the Intelligence Community\u27s (IC) approach to culture, which relies on the cognitive linguistic theories of George Lakoff and colleagues. It also explores the problem of the opacity of these risks for analysts, even as such predictive cultural analytics are becoming a part of intelligence forecasting. This article examines the problem of information secrecy in two ways, by unpacking the opacity of “black box,” algorithm-based social science of culture for end users with little appreciation of their potential biases, and by evaluating the IC\u27s nontransparent approach to foreign cultures, as it underwrites national security assessments