What is Communication Science?

Berger, Charles R.; Roloff, Michael E.; Roskos‐Ewoldsen, David R.

doi:10.4135/9781412982818.n1

Cited by 92 publications

(77 citation statements)

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“…McQuinn et al 2009;Amati et al 2013). A statistical sample of high-z GRBs can provide fundamental information about: the number density and properties of low-mass galaxies (Tanvir et al 2012;Basa et al 2012;Salvaterra et al 2013;Berger et al 2014); the neutral-hydrogen fraction (Gallerani et al 2008;Nagamine et al 2008;McQuinn et al 2008;Robertson & Ellis 2012); the escape fraction of UV photons from high-z galaxies (Chen et al 2007;Fynbo et al 2009); the early cosmic magnetic fields (Takahashi et al 2011), and the level of the local inter-galactic radiation field (Inoue et al 2010 Moreover, GRBs will also allow us to measure independently the cosmic star-formation rate, even above the limits of current and future galaxy surveys (Ishida et al 2011), provided that we are able to quantify the possible biases present in the observed GRB redshift distribution Trenti et al 2013;Vergani et al 2014). Even the detection of a single GRB at z > 10 would be extremely useful to constrain dark matter models (de Souza et al 2013) and primordial non-Gaussianities (Maio et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Accessing the population of high-redshift Gamma Ray Bursts

Ghirlanda

Salvaterra

Ghisellini

et al. 2015

Monthly Notices of the Royal Astronomical Society

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Gamma Ray Bursts (GRBs) are a powerful probe of the high redshift Universe. We present a tool to estimate the detection rate of high-z GRBs by a generic detector with defined energy band and sensitivity. We base this on a population model that reproduces the observed properties of GRBs detected by Swift, Fermi and CGRO in the hard X-ray and γ-ray bands. We provide the expected cumulative distributions of the flux and fluence of simulated GRBs in different energy bands. We show that scintillator detectors, operating at relatively high energies (e.g. tens of keV to the MeV), can detect only the most luminous GRBs at high redshifts due to the link between the peak spectral energy and the luminosity (E peak − L iso ) of GRBs. We show that the best strategy for catching the largest number of high-z bursts is to go softer (e.g. in the soft X-ray band) but with a very high sensitivity. For instance, an imaging soft X-ray detector operating in the 0.2-5 keV energy band reaching a sensitivity, corresponding to a fluence, of ∼ 10 −8 erg cm −2 is expected to detect ≈ 40 GRBs yr −1 sr −1 at z 5 (≈ 3 GRBs yr −1 sr −1 at z 10). Once high-z GRBs are detected the principal issue is to secure their redshift. To this aim we estimate their NIR afterglow flux at relatively early times and evaluate the effectiveness of following them up and construct usable samples of events with any forthcoming GRB mission dedicated to explore the high-z Universe.

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Section: Introductionmentioning

confidence: 99%

Accessing the population of high-redshift Gamma Ray Bursts

Ghirlanda

Salvaterra

Ghisellini

et al. 2015

Monthly Notices of the Royal Astronomical Society

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“…However, while a relatively small body of literature was conducted on communication competencies in the context of online learning, many studies have been conducted in other areas, including a) linguistic communication competencies focusing on grammatical skills (Berger, Roloff, Roskos-Ewoldsen, 2010;Chomsky, 2006;Widdowson, 2007); b) the intercultural communication competencies (Samovar, Porter, & McDaniel, 2010;Tuleja, 2009); c) teacher's communication competencies (Daly & Vangelisti, 2003;Mottet & Beebe, 2006;Wilson & Sabee, 2003); and d) manager's communication competencies in the business sector (Jurado, Eduardo, Luis, & Maribel, 2006;Pavitt, 1999;Rallis & Goldring, 2000). However, those areas were not included in this literature review because they were not directly related to distance learners' communication competencies in online learning.…”

Section: Communication Competenciesmentioning

confidence: 99%

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

Richardson

2015

OLJ

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The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor analysis (EFA) and reliability analysis. Twenty items from three competencies–social competencies, communication competencies, and technical competencies–were designated for the initial instrument based on the Student Online Learning Readiness (SOLR) Model as a new conceptual model. An exploratory factor analysis (EFA) revealed that four factor-structures of the instrument of student readiness in online learning explained 66.69% of the variance in the pattern of relationships among the items. All four factors had high reliabilities (all at or above Cronbach’s Î± > .823). Twenty items remained in the final questionnaire after deleting two items which cross-loaded on multiple factors (social competencies with classmates: five items; social competencies with instructor: five items; communication competencies: four items; and technical competencies: six items). The four-factor structure of the Student Online Learning Readiness (SOLR) instrument has been confirmed through this study. Educators can use the Student Online Learning Readiness (SOLR) instrument in order to gain a better understanding of the level of freshmen college students’ online learning readiness by measuring their social, communication, and technical competencies. In addition, this study examined two factors of social integration in Tinto’s Student Integration Model (SIM) and has introduced the Student Online Learning Readiness (SOLR) conceptual model with the purpose of extending Tinto’s social integration model to online learning environments.

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“…According to Berger et al (2010), a theory of framing effects requires a clear understanding of the processes that produce these effects. Only by knowing these psychological mechanisms can we explain and accurately predict the effects of news frames.…”

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confidence: 99%