Active Learning Strategies for Technology Assisted Sensitivity Review

Government documents must be reviewed to identify and protect any sensitive information, such as personal information, before the documents can be released to the public. However, in the era of digital government documents, such as e-mail, traditional sensitivity review procedures are no longer practical, for example due to the volume of documents to be reviewed. Therefore, there is a need for new technology assisted review protocols to integrate automatic sensitivity classification into the sensitivity review process. Moreover, to effectively assist sensitivity review, such assistive technologies must incorporate reviewer feedback to enable sensitivity classifiers to quickly learn and adapt to the sensitivities within a collection, when the types of sensitivity are not known a priori. In this work, we present a thorough evaluation of active learning strategies for sensitivity review. Moreover, we present an active learning strategy that integrates reviewer feedback, from sensitive text annotations, to identify features of sensitivity that enable us to learn an effective sensitivity classifier (0.7 Balanced Accuracy) using significantly less reviewer effort, according to the sign test (p < 0.01 ). Moreover, this approach results in a 51% reduction in the number of documents required to be reviewed to achieve the same level of classification accuracy, compared to when the approach is deployed without annotation features

Active Learning Stopping Strategies for Technology-Assisted Sensitivity Review

Active learning strategies are often deployed in technology-assisted review tasks, such as e-discovery and sensitivity review, to learn a classifier that can assist the reviewers with their task. In particular, an active learning strategy selects the documents that are expected to be the most useful for learning an effective classifier, so that these documents can be reviewed before the less useful ones. However, when reviewing for sensitivity, the order in which the documents are reviewed can impact on the reviewers' ability to perform the review. Therefore, when deploying active learning in technology-assisted sensitivity review, we want to know when a sufficiently effective classifier has been learned, such that the active learning can stop and the reviewing order of the documents can be selected by the reviewer instead of the classifier. In this work, we propose two active learning stopping strategies for technology-assisted sensitivity review. We evaluate the effectiveness of our proposed approaches in comparison with three state-of-the-art stopping strategies from the literature. We show that our best performing approach results in a significantly more effective sensitivity classifier (+6.6% F2) than the best performing stopping strategy from the literature (McNemar's test, p<0.05)

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Blogging: Promoting Learner Autonomy and Intercultural Competence through Study Abroad

The current study explores closely how using a combined modalities of asynchronous computer-mediated communication (CMC) via blogs and face-to-face (FTF) interaction through ethnographic interviews with native speakers (L1s) supports autonomous learning as the result of reflective and social processes. The study involves 16 American undergraduate students who participated in blogs to develop their intercultural competence over the course of one-semester study abroad. The results show that blogs afforded students the opportunity to work independently (e.g., content creation) and reflect upon cross-cultural issues. Critical reflection, however, relied on the teacher’s guidance and feedback, as most of the students were cognitively challenged by not being able to clearly articulate different points of view. It is likely that students were not accustomed to reflecting. The findings also indicate that task type fostered autonomy in different ways. While free topics gave students more control of their own learning, teacher-assigned topics required them to critically think about the readings. Lack of access to Internet at the host institution and family also contributed to a limited level of social interaction. The study concludes that well-designed tasks, effective metacognitive and cognitive skills, and the accessibility to Internet are essential to maximize the potentials of blogs for learner autonomy and intercultural communication

Contextual Sensitivity in Grounded Theory: The Role of Pilot Studies

Grounded Theory is an established methodological approach for context specific inductive theory building. The grounded nature of the methodology refers to these specific contexts from which emergent propositions are drawn. Thus, any grounded theory study requires not only theoretical sensitivity, but also a good insight on how to design the research in the human activity systems to be studied. The lack of this insight may result in inefficient theoretical sampling or even erroneous purposeful sampling. These problems would not necessarily be critical, as it could be argued that through the elliptical process that characterizes grounded theory, remedial loops would always bring the researcher to the core of the theory. However, these elliptical remedial processes can take very long periods of time and result in catastrophic delays in research projects. As a strategy, this paper discusses, contrasts and compares the use of pilot studies in four different grounded theory projects. Each pilot brought different insights about the context, resulting in changes of focus, guidance to improve data collection instruments and informing theoretical sampling. Additionally, as all four projects were undertaken by researchers with little experience of inductive approaches in general and grounded theory in particular, the pilot studies also served the purpose of training in interviewing, relating to interviewees, memoing, constant comparison and coding. This last outcome of the pilot study was actually not planned initially, but revealed itself to be a crucial success factor in the running of the projects. The paper concludes with a theoretical proposition for the concept of contextual sensitivity and for the inclusion of the pilot study in grounded theory research designs

Relative effectiveness of robot-assisted and standard laparoscopic prostatectomy as alternatives to open radical prostatectomy for treatment of localised prostate cancer : a systematic review and mixed treatment comparison meta-analysis

© 2013 The Authors. BJU International © 2013 BJU International.Peer reviewedPublisher PD

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

The influence of Twitter on lecture engagement and discussion

The research presented in this paper is driven by a desire to increase student interaction and engagement in lecture discussion. The issues relating to the use of Twitter to achieve this goal are outlined. At the outset, the importance of interaction and engagement in learning is established, drawing on a number of educational theories and previous research in the area. Following this, the necessity for action is recognised by critiquing lectures as a forum for this standard of learning. The researcher presents technology as a means to increase student interaction, beginning with Audience Response Systems (ARS). A summary of research carried out on ARS is examined to provide a basis for integrating technology. Following this a review of experiments conducted using Twitter is carried out. Although there is a dearth of research in this area, these provide some insights into the use of this technology and its integration into education. The paper then examines student adoption of Twitter as a means of engagement, outlining the strengths, weaknesses and opportunities for the future. Finally emerging uses of the Twitter platform are examined, allowing the reader glimpse student hopes for future integration