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浅谈初中生物概念教学中深度学习质态的构建 被引量:2
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作者 张云柏 《初中生世界(初中教学研究)》 2021年第12期44-46,共3页
概念是生物学课程内容的基本组成。在概念教学中,教师可以尝试分析概念原型,让学生感知概念形成;整合教学内容,构建概念模型;创设情境,深化概念理解;聚焦问题,深入探究概念。从而促进学生构建深度学习概念的良好质态,回归概念教学的理... 概念是生物学课程内容的基本组成。在概念教学中,教师可以尝试分析概念原型,让学生感知概念形成;整合教学内容,构建概念模型;创设情境,深化概念理解;聚焦问题,深入探究概念。从而促进学生构建深度学习概念的良好质态,回归概念教学的理科属性和科学本质,有效达成教学目标。 展开更多
关键词 初中生物 概念教学 深度学习质态 构建
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普通高中学业发展指导的主要内容和实施路径 被引量:1
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作者 周龙祥 《教学与管理》 北大核心 2020年第16期18-20,共3页
学业发展指导是新时代、新课程、新高考对普通高中提出的新课题、新任务和新要求,通过综合评估学生学习质态,构建学业发展指导机制,组建学业指导导师团队,开展学业指导体验活动,可以激发学生学业发展的内驱力和生长力,促进学生适性发展。
关键词 学业发展指导 学习质态 自我认知
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中职语文教学误区及应对方法
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作者 王云珠 《江苏教育》 2017年第36期45-46,共2页
当前中职语文课堂教学异化现象严重,直接影响着学生的课堂学习质态。'让学习真正发生'的语文教育活动才是'本真的中职语文教育',这不仅要求我们用有效教学策略引发学生的学习兴趣,更要求我们在语文课堂上聚焦于教学目... 当前中职语文课堂教学异化现象严重,直接影响着学生的课堂学习质态。'让学习真正发生'的语文教育活动才是'本真的中职语文教育',这不仅要求我们用有效教学策略引发学生的学习兴趣,更要求我们在语文课堂上聚焦于教学目标的科学设定、开展'为了学生''指向语文''体现语用'的'真正的语文'教学活动。 展开更多
关键词 教学目标 语言与思维发展 语文学习质态
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Quantitative properties of ground-states to an M-coupled system with critical exponent in R^N 被引量:1
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作者 Qihan He Jing Yang 《Science China Mathematics》 SCIE CSCD 2018年第4期709-726,共18页
In this paper, we consider the ground-states of the following M-coupled system:where p_(ij)+q_(ij)=2*:=2 N/(N-2)(N≥3). We prove the existence of ground-states to the M-coupled system. At the same time, we not only gi... In this paper, we consider the ground-states of the following M-coupled system:where p_(ij)+q_(ij)=2*:=2 N/(N-2)(N≥3). We prove the existence of ground-states to the M-coupled system. At the same time, we not only give out the characterization of the ground-states, but also study the number of the ground-states, containing the positive ground-states and the semi-trivial ground-states, which may be the first result studying the number of not only positive ground-states but also semi-trivial ground-states. 展开更多
关键词 ground-states quantitative properties critical exponent
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Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based(HECS)descriptors 被引量:10
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作者 Qian Zhao Maxim Avdeev +1 位作者 Liquan Chen Siqi Shi 《Science Bulletin》 SCIE EI CSCD 2021年第14期1401-1408,M0003,共9页
Rational design of solid-state electrolytes(SSEs)with high ionic conductivity and low activation energy(Ea)is vital for all solid-state batteries.Machine learning(ML)techniques have recently been successful in predict... Rational design of solid-state electrolytes(SSEs)with high ionic conductivity and low activation energy(Ea)is vital for all solid-state batteries.Machine learning(ML)techniques have recently been successful in predicting Li^(+) conduction property in SSEs with various descriptors and accelerating the development of SSEs.In this work,we extend the previous efforts and introduce a framework of ML prediction for E_(a) in SSEs with hierarchically encoding crystal structure-based(HECS)descriptors.Taking cubic Li-argyrodites as an example,an Ea prediction model is developed to the coefficient of determination(R^(2))and rootmean-square error(RMSE)values of 0.887 and 0.02 eV for training dataset,and 0.820 and 0.02 eV for test dataset,respectively by partial least squares(PLS)analysis,proving the prediction power of HECSdescriptors.The variable importance in projection(VIP)scores demonstrate the combined effects of the global and local Li^(+) conduction environments,especially the anion size and the resultant structural changes associated with anion site disorder.The developed E_(a) prediction model directs us to optimize and design new Li-argyrodites with lower Ea,such as Li_(6–x)PS_(5–x)Cl_(1+x)(<0.322 eV),Li_(6+x)PS_(5+x)Br_(1–x)(<0.273 eV),Li_(6+x)PS_(5+x)Br_(0.25)I_(0.75–x)(<0.352 eV),Li_(6+(5–n)y)P_(1–y)N_(y)S_(5)I(<0.420 eV),Li_(6+(5–n)y)As_(1–y)N_(y)S_(5)I(<0.371 eV),Li_(6+(5–n)y)As_(1–y)NySe_(5)I(<0.450 eV),by broadening bottleneck size,invoking site disorder and activating concerted Li+conduction.This analysis shows great potential in promoting rational design of advanced SSEs and the same approach can be applied to other types of materials. 展开更多
关键词 Solid-state electrolytes(SSEs) Hierarchically encoding crystal structurebased (HECS)descriptors Predicting activation energy Cubic Li-argyrodites Machine learning
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Analysis of the Z(4430) as the First Radial Excitation of the Z_c(3900)
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作者 王志刚 《Communications in Theoretical Physics》 SCIE CAS CSCD 2015年第3期325-330,共6页
In this article,we take the Zc(3900) and Z(4430) as the ground state and the first radial excited state of the axial-vector tetraquark states with J^(PC) = 1^(+-),respectively,and study their masses and pole residues ... In this article,we take the Zc(3900) and Z(4430) as the ground state and the first radial excited state of the axial-vector tetraquark states with J^(PC) = 1^(+-),respectively,and study their masses and pole residues with the QCD sum rules by calculating the contributions of the vacuum condensates up to dimension-10 in a consistent way in the operator product expansion.The numerical result favors assigning the Z_c(3900) and Z(4430) as the ground state and first radial excited state of the axial-vector tetraquark states,respectively. 展开更多
关键词 tetraquark state QCD sum rules
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