2,152 research outputs found

    Student-facing learning analytics dashboard for remote lab practical work

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    The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. We present and evaluate an SFLA dashboard designed using the principles of formative assessment to provide feedback to students during remote lab activities. Feedback is based upon graphical visualisations of student actions performed during lab tasks and comparison to expected procedures using TaskCompare - our custom, asymmetric graph dissimilarity measure that distinguishes students who miss expected actions from those who perform additional actions, a capability missing in existing graph distance (symmetrical dissimilarity) measures. Using a total of N=235 student graphs collected during authentic learning in two different engineering courses, we describe the validation of TaskCompare and evaluate the impact of the SFLA dashboard on task completion during remote lab activities. Additionally, we use components of the Motivated Strategies for Learning Questionnaire (MSLQ) as covariates for propensity score matching (PSM) to account for potential bias in self-selection of use of the dashboard. We find that those students who used the SFLA dashboard achieved significantly better task completion rate (nearly double) than those who did not, with a significant difference in TaskCompare score between the two groups (Mann-Whitney U=453.5 , p<0.01 , Cliff's ÎŽ=0.43 , large effect size). This difference remains after accounting for self-selection. We also report students' positive rating of usefulness of the SFLA dashboard for completing lab work is significantly above a neutral response ( S=21.0 , p<0.01 ). These findings provide evidence that our our SFLA dashboard is an effective means of providing formative assessment during remote laboratory activities

    Justice at the margins: witches, poisoners, and social accountability in Northern Uganda

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    Recent responses to people alleged to be ‘witches’ or ‘poisoners’ among the Madi of northern Uganda are compared with those of the 1980s. The extreme violence of past incidents is set in the context of contemporary upheavals and, in effect, encouragement from Catholic and governmental attitudes and initiatives. Mob justice has subsequently become less common. From 2006, a democratic system for dealing with suspects was introduced, whereby those receiving the highest number of votes are expelled from the neighborhood or punished in other ways. These developments are assessed with reference to trends in supporting ‘traditional’ approaches to social accountability and social healing as alternatives to more conventional measures. Caution is required. Locally acceptable hybrid systems may emerge, but when things turn nasty, it is usually the weak and vulnerable that suffer

    Scaling the Equipment and Play Area in Children’s Sport to improve Motor Skill Acquisition: A Systematic Review

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    BACKGROUND: This review investigated the influence of scaling sports equipment and play area (e.g., field size) on children’s motor skill acquisition. METHODS: Peer-reviewed studies published prior to February 2015 were searched using SPORTDiscus and MEDLINE. Studies were included if the research (a) was empirical, (b) involved participants younger than 18 years, (c) assessed the efficacy of scaling in relation to one or more factors affecting skill learning (psychological factors, skill performance and skill acquisition factors, biomechanical factors, cognitive processing factors), and (d) had a sport or movement skills context. Risk of bias was assessed in relation to selection bias, detection bias, attrition bias, reporting bias and other bias. RESULTS: Twenty-five studies involving 989 children were reviewed. Studies revealed that children preferred using scaled equipment over adult equipment (n = 3), were more engaged in the task (n = 1) and had greater self-efficacy to execute skills (n = 2). Eighteen studies demonstrated that children performed skills better when the equipment and play area were scaled. Children also acquired skills faster in such conditions (n = 2); albeit the practice interventions were relatively short. Five studies showed that scaling led to children adopting more desirable movement patterns, and one study associated scaling with implicit motor learning. CONCLUSION: Most of the studies reviewed provide evidence in support of equipment and play area scaling. However, the conclusions are limited by the small number of studies that examined learning (n = 5), poor ecological validity and skills tests of few trials

    Fast rates in statistical and online learning

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    The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditions in learning theory under which fast learning is possible. We show that most of these conditions are special cases of a single, unifying condition, that comes in two forms: the central condition for 'proper' learning algorithms that always output a hypothesis in the given model, and stochastic mixability for online algorithms that may make predictions outside of the model. We show that under surprisingly weak assumptions both conditions are, in a certain sense, equivalent. The central condition has a re-interpretation in terms of convexity of a set of pseudoprobabilities, linking it to density estimation under misspecification. For bounded losses, we show how the central condition enables a direct proof of fast rates and we prove its equivalence to the Bernstein condition, itself a generalization of the Tsybakov margin condition, both of which have played a central role in obtaining fast rates in statistical learning. Yet, while the Bernstein condition is two-sided, the central condition is one-sided, making it more suitable to deal with unbounded losses. In its stochastic mixability form, our condition generalizes both a stochastic exp-concavity condition identified by Juditsky, Rigollet and Tsybakov and Vovk's notion of mixability. Our unifying conditions thus provide a substantial step towards a characterization of fast rates in statistical learning, similar to how classical mixability characterizes constant regret in the sequential prediction with expert advice setting.Comment: 69 pages, 3 figure

    BayesCG As An Uncertainty Aware Version of CG

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    The Bayesian Conjugate Gradient method (BayesCG) is a probabilistic generalization of the Conjugate Gradient method (CG) for solving linear systems with real symmetric positive definite coefficient matrices. We present a CG-based implementation of BayesCG with a structure-exploiting prior distribution. The BayesCG output consists of CG iterates and posterior covariances that can be propagated to subsequent computations. The covariances are low-rank and maintained in factored form. This allows easy generation of accurate samples to probe uncertainty in subsequent computations. Numerical experiments confirm the effectiveness of the posteriors and their low-rank approximations.Comment: 31 Pages including supplementary material (main paper is 22 pages, supplement is 9 pages). Computer codes are available at https://github.com/treid5/ProbNumCG_Sup
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