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本对现行物理设计性实验的不足进行分析,并提出了将元认知理论运用到物理设计性实验的有关策略。 相似文献
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本文结合教学实践,对如何在物理实验中开展设计性实验等问题进行了讨论,认为在物理实验中先少量地进行一些实验方法,实验技能方面的专项训练,能较快提高学生的科学实验能力,可使学生顺利完成设计性实验,更好地发挥设计性实验的功能,达到其教学目的,并对设计性实验开设后的收获和体会进行了总结。 相似文献
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建立设计性物理实验室为教学改革创新路 总被引:1,自引:0,他引:1
设计性物理实验,能使学生在实验方法的考虑,仪器的选配,测量条件的确定等方面受到初步的训练.教学实践证明,学生通过设计性实验,能增强学习的主动性和提高分析问题,解决问题的能力。 相似文献
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在高等工科院校的物理实验教学中,当学生掌握了物理实验的基本知识、基本理论、基本方法和基本实验技能以后,适当进行一些设计性和研究性物理实验的训练是十分必要的.1.设计性物理实验的目的和意义设计性物理实验的教学目的,是要学生在系统复习的基础上,运用所学知识和技能独立解决一个物理实验问题.通过独立分析问题,独立解决问题,使学生把知识转化为能力,为作毕业设计、写科研成果报告和学术论文作初步训练.这对激励学生的创造性和深入研究的探索精神、培养科学实验能力、提高科学实验素质均有重要作用;通过生动活泼的学习和… 相似文献
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中学物理设计性实验题,是近年来出现的一种新颖题型.所谓设计性实验题,就是要求学生灵活地运用已掌握的物理基础知识和实验技能,设计出一个切实可行的实验方案,以达到某一实验目的.对学生进行这方面的训练和考核,将极大地激发学生的学习兴趣,使他们进一步学好物理基础知识,重视实验技能的提高;也将使学生在中学阶段初步学会以严格的科学实验为基础 相似文献
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学生整体元认知能力水平较低,缺乏主动性,对教师的依赖性较强是一种较普遍的教育现状;但成绩较好的学生的元认知水平高于成绩较差的学生,前者能较有效地组织自己的学习策略、制定学习计划,时时地进行反思、评价自己. 相似文献
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The binary perceptron is the simplest artificial neural network formed by N input units and one output unit, with the neural states and the synaptic weights all restricted to ±1 values. The task in the teacher-student scenario is to infer the hidden weight vector by training on a set of labeled patterns. Previous efforts on the passive learning mode have shown that learning from independent random patterns is quite inefficient. Here we consider the active online learning mode in which the student designs every new Ising training pattern. We demonstrate that it is mathematically possible to achieve perfect(error-free) inference using only N designed training patterns, but this is computationally unfeasible for large systems. We then investigate two Bayesian statistical designing protocols, which require 2.3N and 1.9N training patterns, respectively, to achieve error-free inference. If the training patterns are instead designed through deductive reasoning, perfect inference is achieved using N + log_2N samples. The performance gap between Bayesian and deductive designing strategies may be shortened in future work by taking into account the possibility of ergodicity breaking in the version space of the binary perceptron. 相似文献
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《声学学报:英文版》2020,(1)
The study focuses on the effect of auditory target tracking training on selective attention in continuous speech-shaped noise environment.Firstly,a short-term and simplified training method was designed,which adopted stable stimuli tracking to train the participants.After twenty trials,the validity of training method was verified by speech-shaped noise perception under the condition of 3 x 5 noise which is composed of two factors,the type of speech interference and the signal-to-noise ratio(SNR).The experimental results show that the speech perception performance of the training group is significantly better than that of the control group,which proves that the speech perception performance and auditory attention in speechshaped noise environment can be improved through the training of auditory target tracking.This indicates that the auditory target tracking training can stabilize the auditory attention level under the condition of speech-shaped noise.The work provides an effective method for promoting auditory selective attention in the real world and can be extended to specialized vocational training. 相似文献
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Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification. 相似文献
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车牌字符识别是车牌识别系统中的关键环节。采用图像处理和神经网络相结合的方法设计新的车牌字符识别算法,先对分割出的车牌字符进行归一化处理,然后进行SOBEI.边缘检测和角点特征提取,最后输入BP神经网络进行训练、识别,其中BP神经网络模型属于改进型神经网络。通过一系列神经网络训练和仿真实验,车牌识别速度和正确率得到了明显的提高。 相似文献