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Institution

Open University

EducationMilton Keynes, United Kingdom
About: Open University is a education organization based out in Milton Keynes, United Kingdom. It is known for research contribution in the topics: Context (language use) & Population. The organization has 11702 authors who have published 35020 publications receiving 1110835 citations. The organization is also known as: Open University, The & Open University.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors show that fluid-peridotite and fluid-serpentinite interaction processes are an important factor regarding the budget of exchange processes between the lithosphere and the hydrosphere in slow spreading environments.

392 citations

Journal ArticleDOI
TL;DR: In this article, the authors identify the key influencing factors on consumer adoption and effective use of energy efficient products and systems and identify how consumers may avoid or mitigate the rebound effects and how manufacturers, service providers and government might design and promote such products to achieve their optimal environmental benefits.

392 citations

Journal ArticleDOI
TL;DR: The SLIM (Social Learning for the integrated management and sustainable use of water at catchment scale) project as mentioned in this paper is a multidisciplinary group of researchers to research social learning in catchments of different type, scale, and socioeconomic situation.

392 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a review of research comparing the effectiveness of individual learning environments with collaborative learning environments and conclude that learning by an individual becomes less effective and efficient than learning by a group of individuals as task complexity increases, due to differing complexities of the learning tasks used in the research and the concomitant load imposed on the learner's cognitive system.
Abstract: This article presents a review of research comparing the effectiveness of individual learning environments with collaborative learning environments. In reviewing the literature, it was determined that there is no clear and unequivocal picture of how, when, and why the effectiveness of these two approaches to learning differ, a result which may be due to differing complexities of the learning tasks used in the research and the concomitant load imposed on the learner’s cognitive system. Based upon cognitive load theory, it is argued that learning by an individual becomes less effective and efficient than learning by a group of individuals as task complexity increases. Dividing the processing of information across individuals is useful when the cognitive load is high because it allows information to be divided across a larger reservoir of cognitive capacity. Although such division requires that information be recombined and that processing be coordinated, under high load conditions, these costs are minimal compared to the gain achieved by this division of labor. In contrast, under low load conditions, an individual can adequately carry out the required processing activities, and the costs of recombination and coordination are relatively more substantial. Implications of these ideas for research and practice of collaborative learning are discussed.

392 citations

Journal ArticleDOI
Martin F. Miller1
TL;DR: In this article, the relationship between δ17O and δ18O for entity a measured with respect to reference b is accurately represented by the linear function: 1000 ln 1+ δ 17 O 1000 =λ1000 ln [1+k a,b ](α 18/16 ) λ where ka,b is a measure of the offset (if any) of a from the mass-dependent fractionation line, of slope on which b lies.

391 citations


Authors

Showing all 11915 results

NameH-indexPapersCitations
Simon Baron-Cohen172773118071
Rob Ivison1661161102314
David W. Johnson1602714140778
David Scott124156182554
R. Santonico12077767421
Eva K. Grebel11886383915
Chris J. Hawkesworth11236038666
Johannes Brug10962044832
Mark J. Nieuwenhuijsen10764749080
M. Santosh103134449846
Andrew J. King10288246038
Wim H. M. Saris9950634967
Peter Nijkamp97240750826
John Dixon9654336929
Timothy Clark95113753665
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023103
2022395
20211,994
20201,928
20191,810
20181,629