Evaluation of presentation modalities in a hypermedia system

Mann, Bruce L.

doi:10.1016/s0360-1315(96)00037-1

Cited by 16 publications

(10 citation statements)

References 33 publications

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“…The procedure in this study followed those suggested in previous studies of this kind (Mann, 1997a). A number of activities were done simultaneously and the final schedule established at the mutual convenience of the participants and the investigators.…”

Section: Design Of the Studymentioning

confidence: 99%

“…1995), post‐secondary students learned better when auditory narration was presented simultaneously with corresponding diagrams. Mann (1997a) also found that university students learned better from some text with speech and diagrams, than from either speech and diagrams, or text and diagrams. Regarding learning processes, university students working with speech and diagrams made more evaluative‐level verbalisations, than those with text and diagrams (Mann, 1995a).…”

Section: Introductionmentioning

confidence: 94%

“…Previous research has shown that adults and older adolescents (i.e. apprentices, college and university students) learn better from educational multimedia when critical information is presented in speech rather than on‐screen text (Chandler & Sweller, 1992; Mann, 1995a; Mann, 1997a; Mayer, 1997; Moreno & Mayer, 2000). A study with 78 college students (Mayer & Moreno, 1998) reported enhanced learning when the instructional material did not require them to split their attention between multiple sources of mutually referring information.…”

Section: Introductionmentioning

confidence: 99%

“…According to Mayer's (1997) generative theory of multimedia learning, meaningful learning occurs when students select and organise relevant visual and verbal information, and can systematically integrate the newly constructed representations. Students working with sound focused their attention on the critical information needed to make successful interpretations (Mann, 1997a). Mousavi et al (1995) found that students learned better from static diagrams for geometry problems when auditory narration was presented simultaneously with the corresponding diagrams.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of temporal speech and text cueing in educational multimedia

Mann

Newhouse

Pagram

et al. 2002

Computer Assisted Learning

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This research focused on the prediction that children in their school setting would learn more from educational multimedia when critical information was presented as spoken instead of textual cues. Analyses of a study (n= 42) showed that 12‐year‐olds did not learn any more from temporal speech cueing than from temporal text cueing. The findings suggest that multimedia learning for children is a different kind of learning experience than for adults or older adolescents. The results indicate underdeveloped executive control of the referential connections in the children's working memory between reading screen text while listening to spoken cues, and between watching on‐screen animations play while listening to spoken cues. Further study is warranted. Implications may be derived for educational multimedia research in school settings.

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Section: Design Of the Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparison of temporal speech and text cueing in educational multimedia

Mann

Newhouse

Pagram

et al. 2002

Computer Assisted Learning

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“…A recent pilot study (Mann, 1997) compared the relative effectiveness of three versions of hypermedia systems: namely text, sound/text, and sound. The results indicated that those working with sound could focus their attention on the critical information.…”

Section: Introductory Statistics On the Webmentioning

confidence: 99%

Book Reviews

1998

Aus NZ J of Statistics

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By the authors' admission, this monograph is not intended as 'a complete up-to-date account of the Bayesian literature in finite population sampling', but is more a reflection of the authors' 'personal interests'. By inspection of the authors' publications, this is obvious: the focus of the book is on Bayesian sampling with different degrees of prior information (without specifying a prior distribution and using a pseudo-posterior) and resultant admissibility issues.I was surprised. Given such a narrow focus, I feared an esoteric and disjointed treatment of this topic. Instead, I was treated to well-written discussion of the links between standard frequentist and the new Bayesian results, easily-followed graduation between 'Bayesian foundations', the noninformative approach and hierarchical Bayes models, and comprehensive motivation for the more technical ideas in the book. As welcome reinforcement, I was constantly told what to expect from a section and then treated to a discussion of what I had been told. Theorems and proofs were reasonably well integrated into the text, although obviously this became a little more difficult as the complexity of the material increased. Although examples were not a strong feature of the book, simple examples illustrated early points well, and later chapters briefly described 'real' applications. Computational issues, including simulation and MCMC, were also addressed.The basis of the book is the 'Polya posterior', a predictive distribution for the unobserved units in the population given the observed units. If the unobserved and observed units are considered to be exchangeable, this has correspondence with the Beta and Dirichlet distributions and allows a noninformative stepwise Bayesian justification of the usual frequentist inferential procedures. Extensions to the incorporation of different kinds of prior information (about the entire population or about each member of the population, or auxiliary information), stratification, nonresponse, nonparametric models and nonexchangeability are also considered. The reader then progresses to applications of empirical and hierarchical Bayes methods, which the authors particularly promote for small area estimation. Connection is made between the now familiar stepwise Bayes estimators and empirical Bayes estimators, and the hierarchical model is used to connect model-and designbased estimators. Multistage sampling, nested models and generalized linear models are variously discussed.In my opinion, the book is well worth reading and is quite readable, particularly as a reference for a statistician who is researching in the area of finite population sampling, as extension material for an able student, or as a base from which a practitioner might develop new insights and ideas about sampling.

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