Crowd Learning with Candidate Labeling: an EM-based Solution

Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case of doubt, to choose a subset of labels as a way to extract more information from them. In both the traditional and these novel approaches, the reliability of the labelers can be modeled based on the collections of labels that they provide. In this paper, we propose an Expectation-Maximization-based method for crowdsourced data with candidate sets. Iteratively the likelihood of the parameters that model the reliability of the labelers is maximized, while the ground truth is estimated. The experimental results suggest that the proposed method performs better than the baseline aggregation schemes in terms of estimated accuracy.BES-2016-078095 SVP-2014-068574 IT609-13 TIN2016-78365-

Accurate Visuomotor Control below the Perceptual Threshold of Size Discrimination

Background: Human resolution for object size is typically determined by psychophysical methods that are based on conscious perception. In contrast, grasping of the same objects might be less conscious. It is suggested that grasping is mediated by mechanisms other than those mediating conscious perception. In this study, we compared the visual resolution for object size of the visuomotor and the perceptual system. Methodology/Principal Findings: In Experiment 1, participants discriminated the size of pairs of objects once through perceptual judgments and once by grasping movements toward the objects. Notably, the actual size differences were set below the Just Noticeable Difference (JND). We found that grasping trajectories reflected the actual size differences between the objects regardless of the JND. This pattern was observed even in trials in which the perceptual judgments were erroneous. The results of an additional control experiment showed that these findings were not confounded by task demands. Participants were not aware, therefore, that their size discrimination via grasp was veridical. Conclusions/Significance: We conclude that human resolution is not fully tapped by perceptually determined thresholds

Knowledge spaces and learning spaces

How to design automated procedures which (i) accurately assess the knowledge of a student, and (ii) efficiently provide advices for further study? To produce well-founded answers, Knowledge Space Theory relies on a combinatorial viewpoint on the assessment of knowledge, and thus departs from common, numerical evaluation. Its assessment procedures fundamentally differ from other current ones (such as those of S.A.T. and A.C.T.). They are adaptative (taking into account the possible correctness of previous answers from the student) and they produce an outcome which is far more informative than a crude numerical mark. This chapter recapitulates the main concepts underlying Knowledge Space Theory and its special case, Learning Space Theory. We begin by describing the combinatorial core of the theory, in the form of two basic axioms and the main ensuing results (most of which we give without proofs). In practical applications, learning spaces are huge combinatorial structures which may be difficult to manage. We outline methods providing efficient and comprehensive summaries of such large structures. We then describe the probabilistic part of the theory, especially the Markovian type processes which are instrumental in uncovering the knowledge states of individuals. In the guise of the ALEKS system, which includes a teaching component, these methods have been used by millions of students in schools and colleges, and by home schooled students. We summarize some of the results of these applications

Learning from errors: Identifying strategies in a math tutoring system

This study attempts to investigate how students gain knowledge by utilizing help and practice after making errors. We define three types of strategies used by students after errors: help-seeking (requesting two worked examples in the next attempts after an error), practice (solving the problems in the next two attempts after an error), and mixed (first requesting a worked example or first solving a problem in the next two attempts after an error). Our results indicate that the most frequently used strategies are help and mixed strategies. However, the practice strategy and mixed strategies facilitate immediate performance improvement. Additionally, the help strategy was found to interfere with delayed performance

A class of k-modes algorithms for extracting knowledge structures from data

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A Neo-Meadian approach to human agency: relating the social and the psychological in the ontogenesis of perspective-coordinating persons

How can human agency be reconciled with bio-physical determinism? Starting with a discussion of the long standing debate between determinism and agency, we argue that the seeds of a reconciliation can be found in George Herbert Mead’s ideas concerning social acts, perspectives, differentiation, self-other interactivity, and conscious understanding. Drawing on more recent reformulations of Mead’s ideas, we present an integrated account of the ontogenesis of human agency. Human agency, we argue, should be conceptualized in terms of distanciation from immediate experience, and we show how social interactions, institutions and symbolic resources foster the development of agency in increasingly complex ways. We conclude by situating our work in relation to other developmental accounts and the larger project of theorizing and empirically supporting a compatibilist rendering of human agency as the “determined” self-determination of persons