23,360 research outputs found

    Affective learning: improving engagement and enhancing learning with affect-aware feedback

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    This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

    Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems

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    [Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17

    Expert Elicitation for Reliable System Design

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    This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Selection of Statistical Software for Solving Big Data Problems for Teaching

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    The need for analysts with expertise in big data software is becoming more apparent in 4 today’s society. Unfortunately, the demand for these analysts far exceeds the number 5 available. A potential way to combat this shortage is to identify the software sought by 6 employers and to align this with the software taught by universities. This paper will 7 examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and 8 it will outline the cost, training, statistical methods/tests/uses, and specific uses within 9 industry for each of these software. It will further explain implications for universities and 10 students (PDF
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