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中小学生自主学习力理论建构 被引量:3
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作者 姬文广 李明 尚新华 《中国教育学刊》 CSSCI 北大核心 2020年第S02期33-36,39,共5页
近期的重大疫情,让自主学习这一话题再度引起学生、家长和教师的共同关注。基于自主学习领域对"学习能力"的研究,借用学习力领域对"力"的强调,纳入了自我决定理论的"学习动机自主性连续体",提炼出自主学... 近期的重大疫情,让自主学习这一话题再度引起学生、家长和教师的共同关注。基于自主学习领域对"学习能力"的研究,借用学习力领域对"力"的强调,纳入了自我决定理论的"学习动机自主性连续体",提炼出自主学习的能力、动力和阻力三大潜力因子,构建自主学习力的"火箭模型",为后续开发相应的"自主学习力诊断量表"提供整合的理论框架。 展开更多
关键词 自主学习 学习能力 学习动力 学习阻力
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医学生学习现状调查分析——以四川某医学院为例
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作者 桂叶 李慧 刘沛岩 《大学(教学与教育)》 2022年第1期76-80,共5页
为了解医学生学习现状,结合学生在学习中遇到的问题,探究影响医学生学习的因素,文章通过文献研究分析法获得理论基础,将问卷与访谈调查相结合,深入剖析医学生学习现状。调查回收有效问卷372份,有效回收率97.9%,调研显示,不同年级、专业... 为了解医学生学习现状,结合学生在学习中遇到的问题,探究影响医学生学习的因素,文章通过文献研究分析法获得理论基础,将问卷与访谈调查相结合,深入剖析医学生学习现状。调查回收有效问卷372份,有效回收率97.9%,调研显示,不同年级、专业学生在学习状态、学习模式、学习工具使用等方面存在显著差异。研究发现,医学生学习频率随年级呈高—低—低—高趋势,学习工具能辅导并优化学习,学习频繁者更倾向独自学习。通过研究,以期为医学生个人管理能力的提升及学习质量的改进提供建议。 展开更多
关键词 学习频率 学习阻力 学习目标 学习工具 学习模式
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浅谈如何帮助学生缓解学习和考试时的焦虑心理
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作者 刘亚利 《商业文化》 2011年第11X期276-276,共1页
考试有时使得一些学生产生紧张焦虑心情,必然对考试结果产生一定的影响。同时,日常学习过程,由于种种原因有些学生也会由于焦虑心态影响正常的学习和生活。从心理学角度看,上述焦虑作为学习者都要克服和避免,这是本文重点研究的内容。
关键词 缓解 考试焦虑 学习阻力
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One neural network approach for the surrogate turbulence model in transonic flows 被引量:2
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作者 Linyang Zhu Xuxiang Sun +1 位作者 Yilang Liu Weiwei Zhang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2022年第3期38-51,I0002,共15页
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul... With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective. 展开更多
关键词 Deep neural network Turbulence modeling TRANSONIC High Reynolds number
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A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data 被引量:1
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作者 Sheng-liang LU Ning ZHANG +2 位作者 Shui-long SHEN Annan ZHOU Hu-zhong LI 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2020年第6期496-508,共13页
This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was... This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground,independent of theoretical hypotheses and engineering experience.A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground.Then,an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile.The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles,not only under the ultimate load but also under the working load.Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas. 展开更多
关键词 Deep-learning method Cast-in-site pile Shaft resistance Field test Reclaimed ground
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