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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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書(shū)目名稱(chēng)Visualization in Science Education
編輯John K. Gilbert
視頻videohttp://file.papertrans.cn/984/983858/983858.mp4
概述Broad intellectual basis: science, science education, psychology, philosophy.Theory and practice closely integrated.Authors who are active at the frontiers of the field.Authors from 5 countries.First
叢書(shū)名稱(chēng)Models and Modeling in Science Education
圖書(shū)封面Titlebook: Visualization in Science Education;  John K. Gilbert Book 20051st edition Springer Science+Business Media B.V. 2005 experiment.media.modeli
描述.“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 the ever-increasing use of computer-managed modelling packages, especially in chemistry. The first section explores the significance and intellectual standing of visualization. The second section shows how the skills of visualization have been developed practically in science education. This is followed by accounts of how the educational value of visualization has been integrated into university courses in physics, genomics, and geology. The fourth section documents experimental work on the classroom assessment of visualization. An endpiece summarises some of the research and development needed if the contribution of this set of universal skills is to be fully exploited at all levels and in all science subjects..
出版日期Book 20051st edition
關(guān)鍵詞experiment; media; modeling; science; science education; visualization
版次1
doihttps://doi.org/10.1007/1-4020-3613-2
isbn_softcover978-1-4020-5882-0
isbn_ebook978-1-4020-3613-2Series ISSN 1871-2983 Series E-ISSN 2213-2260
issn_series 1871-2983
copyrightSpringer Science+Business Media B.V. 2005
The information of publication is updating

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John K Gilberttill very challenging because of two reasons. First, the ground truth of missing data is actually inaccessible, which makes most imputation methods hard to be trained. Second, incomplete data would easily mislead the model to learn unreliable spatial-temporal dependencies, which finally hurts the im
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Barbara Tverskyr each utterance in a dialogue among four label candidates: joy, sadness, anger, and neutral. A variety of models have been proposed to solve this task, but few of them manage to capture contextual information in a dialogue properly. Therefore, we adopt a Bi-directional Long Short-Term Memory networ
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Michael Briggs,Geogre Bodnerch exploits the statistical properties of items’ existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-s
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