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Titlebook: Visualization in Science Education; John K. Gilbert Book 20051st edition Springer Science+Business Media B.V. 2005 experiment.media.modeli

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樓主: Body-Mass-Index
41#
發(fā)表于 2025-3-28 15:56:57 | 只看該作者
Robert Kozma,Joel Russell designed to remove “weak” instances selected in previous rounds. Experiments were undertaken on nine benchmark datasets. The results show that SPOR is superior to both popular co-training regression methods and state-of-the-art semi-supervised regressors.
42#
發(fā)表于 2025-3-28 18:47:04 | 只看該作者
Galit Botzer,Miriam Reineron a proposed two-layer structure, CODE is able to detect the hateful texts with observed coded words as well as newly emerged coded words. Experimental results on a Twitter dataset show the effectiveness of our approach.
43#
發(fā)表于 2025-3-29 01:38:32 | 只看該作者
Mental Models: Theoretical Issues for Visualizations in Science Educationmation. In addition, these factors are related directly to the use (and potential) of visualizations as educational methodologies. The chapter concludes with several challenges for future work on visualizations in science education.
44#
發(fā)表于 2025-3-29 05:14:13 | 只看該作者
45#
發(fā)表于 2025-3-29 10:21:26 | 只看該作者
A Model of Molecular Visualizationerents, relations, results, and rules/syntax. The fifth component is dynamic: operation. Two examples of operation are visualization and rotation. Participants used the constructed mental models as mental tools to complete the task. This conceptualization of mental model construction constitutes a theory of learning.
46#
發(fā)表于 2025-3-29 14:33:33 | 只看該作者
47#
發(fā)表于 2025-3-29 17:40:23 | 只看該作者
1871-2983 t the frontiers of the field.Authors from 5 countries.First .“Visualization in Science Education” draws on the insights from cognitive psychology, science, and education, by experts from Australia, Israel, Slovenia, UK, and USA. It unites these with the practice of science education, particularly th
48#
發(fā)表于 2025-3-29 22:21:42 | 只看該作者
Michael Briggs,Geogre Bodnerputational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset.
49#
發(fā)表于 2025-3-30 02:09:38 | 只看該作者
50#
發(fā)表于 2025-3-30 04:31:43 | 只看該作者
1871-2983 if the contribution of this set of universal skills is to be fully exploited at all levels and in all science subjects..978-1-4020-5882-0978-1-4020-3613-2Series ISSN 1871-2983 Series E-ISSN 2213-2260
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