A Learner-Centred Approach for Lifelong Learning Powered by the Blockchain

The emergence of Blockchain technology promises to revolutionise not only the financial world, but also lifelong learning in many different ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and training services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This paper explores the different aspects of lifelong learning that are affected by this new paradigm and describes an ecosystem that places the learner at the centre of the learning process and its associated data. More specifically, we outline the ways that ePortfolios, accreditation and tutoring can evolve within this learner-centred ecosystem and we discuss the various benefits that this evolution bears for lifelong learners

Implicit theories of a desire for fame

The aim of the present studies was to generate implicit theories of a desire for fame among the general population. In Study 1, we were able to develop a nine-factor analytic model of conceptions of the desire to be famous that initially comprised nine separate factors; ambition, meaning derived through comparison with others, psychologically vulnerable, attention seeking, conceitedness, social access, altruistic, positive affect, and glamour. Analysis that sought to examine replicability among these factors suggested that three factors (altruistic, positive affect, and glamour) neither display factor congruence nor display adequate internal reliability. A second study examined the validity of these factors in predicting profiles of individuals who may desire fame. The findings from this study suggested that two of the nine factors (positive affect and altruism) could not be considered strong factors within the model. Overall, the findings suggest that implicit theories of a desire for fame comprise six factors. The discussion focuses on how an implicit model of a desire for fame might progress into formal theories of a desire for fame

New science on the Open Science Grid

The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large-scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement, and the distributed facility. This paper gives both a brief general description and specific examples of new science enabled on the OSG. More information is available at the OSG web site: www.opensciencegrid.org

The Effect of Cross-age Tutoring on Reading Attitude

One of the greatest challenges facing teachers of reading today is the negative attitude of students toward reading. One suggested means of positively influencing the reading attitude of students is cross-age tutoring. However, a study is needed to establish whether a clear link exists between cross-age tutoring and positive changes in reading attitude. Experimental research was conducted during the course of an academic quarter (nine weeks) to determine whether cross-age tutoring has a positive impact on reading attitude. The subjects of the study were first grade students (n=12). The first graders were identified for the study based on low scores on the Elementary Reading Attitude Survey. The first graders were placed in matched pairs based on their Elementary Reading Attitude Survey raw scores. Matched pairs were then randomly split into a control group (n=6) and an experimental group (n=6). The tutors were second grade students (n=6) identified through teacher interviews as being enthusiastic and skilled readers. During four 30-minute training sessions, the second grade tutors were trained to implement a two part instructional plan during each tutoring session. The instructional plan included sight word practice, word games, paired reading time with retelling, and testing in the Accelerated Reader computer program. Throughout the nine weeks of the study, the second grade tutors conducted two 30 minute sessions each week with students in the experimental group. During the tutoring sessions, first grade students in the control group engaged in typical independent reading activities such as sustained silent reading. All first grade subjects were retested with the Elementary Reading Attitude Survey following the last tutoring session. A Wilcoxon matched-pairs signed-ranks test was used to analyze the posttest data. In addition, qualitative data were obtained through observational rating scales of reading behaviors completed by a certified teacher acting as a teaching assistant in the classroom. Results indicate that students in the experimental group did show greater increases in reading attitude than those in the control group. However, the Wilcoxon test indicated that these differences were not statistically significant

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

Control of hyperglycaemia in paediatric intensive care (CHiP): study protocol.

BACKGROUND: There is increasing evidence that tight blood glucose (BG) control improves outcomes in critically ill adults. Children show similar hyperglycaemic responses to surgery or critical illness. However it is not known whether tight control will benefit children given maturational differences and different disease spectrum. METHODS/DESIGN: The study is an randomised open trial with two parallel groups to assess whether, for children undergoing intensive care in the UK aged <or= 16 years who are ventilated, have an arterial line in-situ and are receiving vasoactive support following injury, major surgery or in association with critical illness in whom it is anticipated such treatment will be required to continue for at least 12 hours, tight control will increase the numbers of days alive and free of mechanical ventilation at 30 days, and lead to improvement in a range of complications associated with intensive care treatment and be cost effective. Children in the tight control group will receive insulin by intravenous infusion titrated to maintain BG between 4 and 7.0 mmol/l. Children in the control group will be treated according to a standard current approach to BG management. Children will be followed up to determine vital status and healthcare resources usage between discharge and 12 months post-randomisation. Information regarding overall health status, global neurological outcome, attention and behavioural status will be sought from a subgroup with traumatic brain injury (TBI). A difference of 2 days in the number of ventilator-free days within the first 30 days post-randomisation is considered clinically important. Conservatively assuming a standard deviation of a week across both trial arms, a type I error of 1% (2-sided test), and allowing for non-compliance, a total sample size of 1000 patients would have 90% power to detect this difference. To detect effect differences between cardiac and non-cardiac patients, a target sample size of 1500 is required. An economic evaluation will assess whether the costs of achieving tight BG control are justified by subsequent reductions in hospitalisation costs. DISCUSSION: The relevance of tight glycaemic control in this population needs to be assessed formally before being accepted into standard practice

Neurophysiology

Contains research objectives and summary of research on sixteen research projects.National Institutes of Health (Grant 5 TO1 EY00090-03)National Institutes of Health (Grant 3 RO1 EY01149-03S1)Bell Laboratories (Grant)National Institutes of Health (Grant 5 RO1 NS12307-02)National Institutes of Health (Grant K04 NS00010

Smart Blockchain Badges for Data Science Education

Blockchain technology has the potential to revolutionise education in a number of ways. In this paper, we explore the applications of Smart Blockchain Badges on data science education. In particular, we investigate how Smart Blockchain Badges can support learners that want to advance their careers in data science, by offering them personalised recommendations based on their learning achievements. This work aims at enhancing data science accreditation by introducing a robust system based on the Blockchain technology. Learners will benefit from a sophisticated, open and transparent accreditation system, as well as from receiving job recommendations that match their skills and can potentially progress their careers. As a result, this work contributes towards closing the data science skills gap by linking data science education to the industry

Blockchain Applications in Lifelong Learning and the Role of the Semantic Blockchain

The emergence of the blockchain promises to revolutionise not only the financial world but also lifelong learning in various ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and tutoring services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This chapter explores the different aspects of lifelong learning that are affected by this new paradigm and describes an ecosystem that places the learner at the centre of the learning process and its associated data. This chapter also discusses the possibilities that will be afforded by the combination of trustworthy educational data enhanced with meaningful web-accessible linked data, and what these developments will mean for learners, educators, and the employment market