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在酸、碱、盐教学中以复分解反应条件教学为例, 应用“微粒观”统领酸、碱、盐教学。基于“微粒观”构建了本节课的知识结构层级图, 剖析了学生的认识发展过程, 通过教学实践总结、归纳了若干教学策略, 从而突破学生学习难点, 提高学生的迁移和应用能力, 并为高中进一步学习离子反应做好铺垫。 相似文献
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Digital soil mapping (DSM) increasingly makes use of machine learning algorithms to identify relationships between soil properties and multiple covariates that can be detected across landscapes. Selecting the appropriate algorithm for model building is critical for optimizing results in the context of the available data. Over the past decade, many studies have tested different machine learning (ML) approaches on a variety of soil data sets. Here, we review the application of some of the most popular ML algorithms for digital soil mapping. Specifically, we compare the strengths and weaknesses of multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), Cubist, random forest (RF), and artificial neural networks (ANN) for DSM. These algorithms were compared on the basis of five factors: (1) quantity of hyperparameters, (2) sample size, (3) covariate selection, (4) learning time, and (5) interpretability of the resulting model. If training time is a limitation, then algorithms that have fewer model parameters and hyperparameters should be considered, e.g., MLR, KNN, SVR, and Cubist. If the data set is large (thousands of samples) and computation time is not an issue, ANN would likely produce the best results. If the data set is small (<100), then Cubist, KNN, RF, and SVR are likely to perform better than ANN and MLR. The uncertainty in predictions produced by Cubist, KNN, RF, and SVR may not decrease with large datasets. When interpretability of the resulting model is important to the user, Cubist, MLR, and RF are more appropriate algorithms as they do not function as “black boxes.” There is no one correct approach to produce models for predicting the spatial distribution of soil properties. Nonetheless, some algorithms are more appropriate than others considering the nature of the data and purpose of mapping activity. 相似文献
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在“氮及其化合物”教学中,利用社会性科学议题“论证重雾霾天气‘汽车限行’的合理性”,通过直面议题激发原始想法、探讨汽车尾气与雾霾是否存在关系、探讨汽车尾气是不是导致雾霾的主要原因、面对议题的科学决策的具体教学环节,促进学生掌握氮及其化合物的主要性质,建立从物质类别、元素价态视角研究物质性质及转化的思路方法,促进多个维度核心素养的融合发展。经过多轮次教学改进,结合教学实践过程及其教学效果抽提出真实情境的创设、驱动性问题的设计、课前、课上和课后3个阶段统筹安排等教学策略。 相似文献
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以制作天气瓶为项目核心与溶液单元知识进行整体融合。经过认识天气瓶、揭秘晶体变化奥秘和自制天气瓶等一系列活动,学习多角度认识溶液的组成、溶解度和科学的定量实验设计方法。溶液形成过程的微观本质是学生的学习难点,应用多个电导率仪测定不同位置的离子浓度变化,形成硝酸钾溶解过程曲线,通过分析曲线数据变化很好地搭建起宏观辨识与微观探析间的桥梁,帮助学生突破学习难点。学生在解决这一复杂、综合实际问题的过程中,有效地发展了宏观辨识、变化观念、证据推理、科学探究、创新精神等化学学科核心素养,发展了科学价值观。 相似文献
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《Tetrahedron》2006,62(2-3):375-380
The aza-Henry reaction of imines with nitromethane was promoted by cinchona alkaloids and modified cinchona bases to give optically active β-nitroamines. Various N-protected imines were examined as substrates. N-Boc, N-Cbz, and N-Fmoc protected imines gave the best results in terms of chemical yields and enantioselectivities. After a careful screening of a series of chiral bases, very good enantioselectivities up to 94% ee were obtained using a cinchona-based thiourea organocatalyst under the optimized reaction conditions. 相似文献
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