A grid-based approach for processing group activity log files

The information collected regarding group activity in a collaborative learning environment requires classifying, structuring and processing. The aim is to process this information in order to extract, reveal and provide students and tutors with valuable knowledge, awareness and feedback in order to successfully perform the collaborative learning activity. However, the large amount of information generated during online group activity may be time-consuming to process and, hence, can hinder the real-time delivery of the information. In this study we show how a Grid-based paradigm can be used to effectively process and present the information regarding group activity gathered in the log files under a collaborative environment. The computational power of the Grid makes it possible to process a huge amount of event information, compute statistical results and present them, when needed, to the members of the online group and the tutors, who are geographically distributed.Peer ReviewedPostprint (author's final draft

Event based text mining for integrated network construction

The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the networ

Sparking Innovations in Management

{Excerpt} Gary Hamel defines management innovation as a marked departure from traditional management principles, processes, and practices (or a departure from customary organizational forms that significantly alters the way the work of management is performed). He deems it the prime driver of sustainable competitive advantage in the 21st century

Disrupting the dissertation: linked data, enhanced publication and algorithmic culture

This article explores how the three aspects of Striphas’ notion of algorithmic culture (information, crowds and algorithms) might influence and potentially disrupt established educational practices.  We draw on our experience of introducing semantic web and linked data technologies into higher education settings, focussing on extended student writing activities such as dissertations and projects, and drawing in particular on our experiences related to undergraduate archaeology dissertations. The potential for linked data to be incorporated into electronic texts, including academic publications, has already been described, but these accounts have highlighted opportunities to enhance research integrity and interactivity, rather than considering their potential creatively to disrupt existing academic practices. We discuss how the changing relationships between subject content and practices, teachers, learners and wider publics both in this particular algorithmic culture, and more generally, offer new opportunities; but also how the unpredictability of crowds, the variable nature and quality of data, and the often hidden power of algorithms, introduce new pedagogical challenges and opportunities