278,971 research outputs found

    Pedagoginės praktikos metu studentų patiriami sunkumai: klausimyno struktūros psichometrinės savybės

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    The aim of our study was to describe the psychometric properties of the structure of a questionnaire designed to assess the difficulties experienced by pre-service teachers during their pedagogical practice. The study involved 110 pre-service teachers (university students): 96 women and 14 men, with a mean age of 24,28 years (SD = 5,03). Based on our previous research, a set of 20 items was developed asking students to rate their experience on a five-point scale. The hierarchical items cluster analysis algorithm ICLUST was used to test the psychometric characteristics of the questionnaire structure. The analysis was performed according to Revelle’s guidelines. Due to the ordinal nature of items, the cluster analysis was performed on the basis of a polychoric correlation matrix. The statistical analysis was carried out using the psych package in R. The reliability of the clusters was assessed taking into account Cronbach’s alpha and Revelle’s beta indicators. Cluster fit, pattern fit, and RMSR were selected as model fit indicators. Two models were developed: the seven-cluster model and two-cluster model. The seven-cluster model consisted of the following clusters: search and selection of material (K1; α = 0,75, β = 0,75), selection of teaching methods (K10; α = 0,77, β = 0,69), classroom management (K13; α = 0,81, β = 0,72), lack of subject and pedagogical knowledge (K3; α = 0,67, β = 0,67), emotions (K4; α = 0,75, β = 0,75), time management (K12; α = 0,70, β = 0,62), and organizational difficulties (K11; α = 0,66, β = 0,60). The two-cluster model consisted of the following clusters: search and selection of material (K1; α = 0,75, β = 0,75) and general difficulties (K18; α = 0,85, β =0,75). Combining 20 items into seven clusters allowed to achieve the best psychometric characteristics of the questionnaire and to reliably assess the difficulties experienced by pre-service teachers during their pedagogical practice. The characteristics of the two-cluster model were satisfactory, and the 18-items general difficulty scale of this model can be chosen as an alternative to calculate the overall estimate of the difficulties experienced by pre-service teachers during their pedagogical practice.Šiame straipsnyje pristatomo tyrimo tikslas – patikrinti pedagoginės praktikos metu studentų patiriamų sunkumų klausimyno struktūros psichometrines savybes. Tyrime dalyvavo pedagoginę praktiką atlikę pedagoginių studijų studentai (N = 110). Duomenys analizuoti pasitelkus hierarchinės teiginių klasterių analizės algoritmą ICLUST. Gauti rezultatai patvirtino, kad dvidešimties teiginių klausimyną sudaro septynios skalės: informacijos paieškos, ugdymo metodų parinkimo, klasės valdymo, žinių trūkumo, emocijų, laiko valdymo ir organizacinių sunkumų. Gautas didelis teiginių suderintumas ir homogeniškumas bei geros bendros modelio savybės. Taip pat straipsnyje pasiūlytas alternatyvus dviejų faktorių modelis, kurio viena aštuoniolikos teiginių skalė leidžia įvertinti bendruosius pedagoginės praktikos metu studentų patirtus sunkumus. Tyrimo rezultatai patvirtina, kad klausimynas yra tinkamas patikimai įvertinti pedagoginės praktikos metu studentų patiriamus sunkumus

    Bayesian inference for queueing networks and modeling of internet services

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    Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where each queue models one of the computers in the system. A key challenge is that the data are incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo. We evaluate our framework on data from a benchmark Web application. We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

    HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

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    High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

    Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures

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    The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper provides a dynamic optimization approach for enhanced cloud infrastructures, based on the concept of cloudlets, which are located at hotspot areas throughout a metropolitan area. In conjunction, we consider classical remote data centers that are rigid with respect to QoS but provide nearly abundant computation resources. Given fluctuating user demands, we optimize the cloudlet placement over a finite time horizon from a cloud infrastructure provider's perspective. By the means of a custom tailed heuristic approach, we are able to reduce the computational effort compared to the exact approach by at least three orders of magnitude, while maintaining a high solution quality with a moderate cost increase of 5.8% or less
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