The science and technology of corpus, and corpus for science and technology

Corpus Linguistics is a young discipline. The earliest work was done in the 1960s, but corpora only began to be widely used by lexicographers and linguists in the late 1980s, by language teachers in the late 1990s, and by language students only very recently. This course in corpus linguistics was held at the Departamento de Linguistica Aplicada, E.T.S.I. de Minas, Universidad Politecnica de Madrid from June 15-19 1998. About 45 teachers registered for the course. 30% had PhDs in linguistics, 20% in literature, and the rest were doctorandi or qualified English teachers. The course was designed to introduce the use of corpora and other computational resources in teaching and research, with special reference to scientific and technological discourse in English. Each participant had a computer networked with the lecturer’s machine, whose display could be projected onto a large screen. Application programs were loaded onto the central server, and telnet and a web browser were available. COBUILD gave us permission to access the 323 million word Bank of English corpus, Mike Scott allowed us to use his Wordsmith Tools software, and Tim Johns gave us a copy of his MicroConcord program.

Organization studies as an applied science:the generation and use of academic knowledge about organizations : introduction to the special issue

The relationship between theory and practice has been discussed in the social sciences for generations. Academics from management and organization studies regularly lament the divide between theory and practice. They regret the insufficient academic knowledge of managerial problems and their solutions, and criticize the scholarly production of theories that are not relevant for organizational practice (Hambrick 1994). Despite the prevalence of this topic in academic discourse, we do not know much about what kind of academic knowledge would be useful to practice, how it would be produced and how the transfer of knowledge between theory and practice actually works. In short, we do not know how we can make academic work more relevant for practice or even whether this would be desirable. In this introduction to the Special Issue, we apply philosophical, theoretical and empirical perspectives to examine the challenges of studying the generation and use of academic knowledge. We then briefly describe the contribution of the seven papers that were selected for this Special Issue. Finally, we discuss issues that still need to be addressed, and make some proposals for future avenues of research.

People, technology, processes and risk knowledge sharing

The present global economic crisis creates doubts about the good use of accumulated experience and knowledge in managing risk in financial services. Typically, risk management practice does not use knowledge management (KM) to improve and to develop new answers to the threats. A key reason is that it is not clear how to break down the “organizational silos” view of risk management (RM) that is commonly taken. As a result, there has been relatively little work on finding the relationships between RM and KM. We have been doing research for the last couple of years on the identification of relationships between these two disciplines. At ECKM 2007 we presented a general review of the literature(s) and some hypotheses for starting research on KM and its relationship to the perceived value of enterprise risk management. This article presents findings based on our preliminary analyses, concentrating on those factors affecting the perceived quality of risk knowledge sharing. These come from a questionnaire survey of RM employees in organisations in the financial services sector, which yielded 121 responses. We have included five explanatory variables for the perceived quality of risk knowledge sharing. These comprised two variables relating to people (organizational capacity for work coordination and perceived quality of communication among groups), one relating to process (perceived quality of risk control) and two related to technology (web channel functionality and RM information system functionality). Our findings so far are that four of these five variables have a significant positive association with the perceived quality of risk knowledge sharing: contrary to expectations, web channel functionality did not have a significant association. Indeed, in some of our exploratory regression studies its coefficient (although not significant) was negative. In stepwise regression, the variable organizational capacity for work coordination accounted for by far the largest part of the variation in the dependent variable perceived quality of risk knowledge sharing. The “people” variables thus appear to have the greatest influence on the perceived quality of risk knowledge sharing, even in a sector that relies heavily on technology and on quantitative approaches to decision making. We have also found similar results with the dependent variable perceived value of Enterprise Risk Management (ERM) implementation.

Technology enhanced learning in Higher Education

Technology-Enhanced Learning in Higher Education is an anthology produced by the international association, Learning in Higher Education (LiHE). LiHE, whose scope includes the activities of colleges, universities and other institutions of higher education, has been one of the leading organisations supporting a shift in the education process from a transmission-based philosophy to a student-centred, learning-based approach. Traditionally education has been envisaged as a process in which the teacher disseminates knowledge and information to the student, and directs them to perform – instructing, cajoling, encouraging them as appropriate – despite different students’ abilities. Yet higher education is currently experiencing rapid transformation, with the introduction of a broad range of technologies which have the potential to enhance student learning. This anthology draws upon the experiences of those practitioners who have been pioneering new applications of technology in higher education, highlighting not only the technologies themselves but also the impact which they have had on student learning. The anthology illustrates how new technologies – which are increasingly well-known and accepted by today’s ‘digital natives’ undertaking higher education – can be adopted and incorporated. One key conclusion is that learning remains a social process even in technology-enhanced learning contexts. So the technology-based proxies we construct need to retain and reflect the agency of the teacher. Technology-Enhanced Learning in Higher Education showcases some of the latest pedagogical technologies and their most creative, state-of-the-art applications to learning in higher education from around the world. Each of the chapters explores technology-enhanced learning in higher education in terms of either policy or practice. They contain detailed descriptions of approaches taken in very different curriculum areas, and demonstrate clearly that technology may and can enhance learning only if it is designed with the learning process of students at its core. So the use of technology in education is more linked to pedagogy than it is to bits and bytes.

Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.