Тест по психологии (10 класс) на тему: Справка Адаптация учащихся 10 класса МКОУ «СШ №1 г.Теберды им. И. П. Крымшамхалова» в 2018-2019 учебном году

Справка

  Адаптация учащихся 10 класса  

МКОУ «СШ №1 г.Теберды им. И. П. Крымшамхалова»  

в 2018-2019 учебном  году

Цель проверки: проверить готовность обучающихся 10 класса на уровне основного общего образования, их адаптацию, единство требований учителей, подготовку к ЕГЭ.

Формы и методы проверки: посещение уроков по математике, физике, русскому языку, беседы с классным  руководителем, анкетирование учащихся.

В 10 классе обучается 18 учащихся:  

В десятом классе происходит резкая  смена критериев оценки знаний  учащихся. Слабоуспевающие ученики обычно покидают школу после девятого класса, требования педагогов увеличиваются, изучаемый материал усложняется.

Наблюдения за учащимися во время уроков показали, что в классе есть некоторое  количество учащихся, имеющие хорошую мотивацию к обучению на уровне среднего.

Подтверждает  опросник, составленный В.С. Юркевич, где  можно определить, насколько у  старшеклассника развита потребность в познании и понимает ли он необходимость обучения в школе лично для себя. Опрошено 17 учащихся из 18.

Определение уровня познавательной потребности

1. Связаны ли твои интересы (в учебе и во внешкольных занятиях) с        выбором будущей профессии?

А. Связаны очень тесно.

Б. Связаны, но не хватает времени, хватаюсь то за одно, то за другое,  нет четкого плана действий.

В. Никак не связаны.

2. Случается ли, что ты обращаешься к серьезным источникам: пользуешься научной литературой, словарями?

А. Часто, Б. Иногда. В. Никогда.

3. Ставишь ли ты в своей учебной работе задачи, выполнение которых невозможно «за один присест», требует кропотливой работы в течение многих дней?

А. Очень часто.

Б. Бывает, что ставлю такие задачи, но редко выполняю их.

В. Не ставлю долговременных задач.

4. Занимаясь любимым делом, можешь ли ты делать «черную», неинтересную работу (выполнять длительные и скучные вычисления при решении интересной задачи)?

А. Делаю такую работу спокойно, в том объеме, в котором это необходимо.

Б.  иногда делаю, иногда бросаю на полпути.

В. Избегаю  выполнения такой работы.

5. Можешь ли ты  заниматься, длительное время интеллектуальной рабой; иногда даже жертвуя развлечениями, а иногда и отдыхом?

А. Всегда, когда это необходимо

Б. Только иногда.        

В. Никогда.

 

Преобладание  ответов «Б» говорит о том, что у старшеклассника развита любознательность и есть интерес к некоторым учебным предметам. Но такой интерес не имеет организованного характера, он хаотичен. У школьника нет понимания того, как в дальнейшем в его жизни могут понадобиться те или иные знания. Таких ответов – 9.

Ответы «В» сигнализируют о низком уровне развития познавательной потребности и о нежелании учиться. Конечно, если «покопаться», всегда найдется область знаний или интеллектуальное занятие, которое в какой-то мере интересует старшеклассника. Таких ответов нет.

Признаки дезадаптации

Появляющиеся у учащихся 10-х классов негативные явления (дисциплинарные проблемы, резкое падение интереса к учению, рост тревожности, дезориентация в жизненных ситуациях) – это не типичные признаки возрастного кризиса подросткового возраста, а следствие образовательного процесса. Чтобы выявить признаки дезадаптации в

 десятом классе провела опросник С.В.Левченко «Чувства в школе»

Задание: из 16 перечисленных чувств выбрать только 8, которые старшеклассник наиболее часто испытывает в школе.

	Я испытываю в школе	10 кл. – 19 ч из 21 ч
1	Спокойствие	10	53 %
2	Усталость	16	84 %
3	Скуку	9	47 %
4	Радость	10	53 %
5	Уверенность в себе / неуверенность	6 / 13	32 % / 68 %
6	Беспокойство	8	42 %
7	Неудовлетворенность собой	8	42 %
8	Раздражение	9	53 %
9	Сомнение	14	74 %
10	Обиду	1	5 %
11	Чувство унижения	1	5 %
12	Страх	—	—
13	Тревогу за будущее	8	42 %
14	Благодарность	11	58 %
15	Симпатию к учителям	5	26 %
16	Желание приходить сюда	6	32 %

 Некоторые  учащихся 10-го класса подвержены дезадаптации:  невнимательны,  тревожны, неуверенны в себе, неудовлетворены  собой, имеют сомнения, тревожатся за будующее.

РЕКОМЕНДАЦИИ педагогам  10 класса

Основные потребности подростков:

1.Самоутверждение среди сверстников.

2.Самореализация в общении.

3.Развитие независимости и самостоятельности.

4.Стремление к смене впечатлений и поиск острых ощущений.

5.Самопознание, саморазвитие и самосовершенствование.

6.Достижение личного благополучия и обретение материальных благ.

Основные проблемы подростков:

1.Сложности в дружеских отношениях и любви.

2.Отношения с родителями и другими взрослыми («не разрешают, не отпускают, не понимают, не дают, заставляют» и т. п.).

3.Проблемы самооценки и развития характера (недовольство собой, своим внешним видом и поведением).

4.Ощущение перегрузки, усталость (неумение контролировать желания и организовывать свое время, неумение учиться; физические недомогания, связанные с перестройкой организма).

5.Проблемы с учебой (снижение успеваемости, нежелание учиться).

6.Выбор жизненного пути (где продолжать учебу, выбор профессии).

7.Проведение досуга.

        

 Педагог-психолог :       Р.А.Чомаева

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Review a Practice Test 90014 90015 90002 90003 90002 If you want to take your prep to the next level, go ahead and review an official practice test. Do not worry about taking it under the normal timing and resource constraints. Instead, think of it as a very initial exposure to the test format and content. 90003 90002 90003 90002 Sit down with the test over the course of several days and tackle it bit by bit. Do not think of it as an actual SAT, or even as a good indicator of how you might perform on the actual SAT.Instead, think of it as a resource to learn more about the test. 90003 .90000 Deep Domain Adaptation In Computer Vision | by Branislav Holländer 90001 90002 90003 During the last decade, the field of Computer vision has made huge progress. This progress is mostly due to the undeniable effectiveness of Convolutional Neural Networks (CNNs). CNNs allow for very precise predictions if trained with high-quality annotated training data. In a classification setting for instance, you would often use one of the standard network architectures out there (ResNet, VGG etc.) and train it using your dataset.This would likely lead to very good performance. 90004 90003 On the other hand, if you do not have huge manually annotated datasets for your particular problem at your disposal, CNNs also allow for making use of others who already trained a network for a similar problem by transfer learning. In this case, you would take a network that is pretrained on a large dataset and tune some of its upper layers using your own small annotated dataset. 90004 90003 Both of these approaches assume that your training data (be it large or small) is representative of the underlying distribution.However, if the inputs at test time differ significantly from the training data, the model might not perform very well. Let us assume, for instance, that you are an autonomous vehicle engineer and you want to segment the images captured by the car’s camera in order to know what lies ahead (buildings, trees, other cars, pedestrians, traffic lights etc.). You have nice human-generated annotations for your NYC dataset and you train a large network using these annotations. You test your self-driving car on the streets of Manhattan and everything seems to work fine.Then you test the same system in Paris and suddenly things go very wrong. The car is not able to detect traffic lights anymore, the cars look very different (there are no yellow cabs in Paris) and the streets are not as straight anymore. 90004 90003 The reason your model did not perform very well in these scenarios is that the problem domain changed. In this particular case, the domain of the input data changed while the task domain (the labels) remained the same. On other occasions, you may want to use data from the same domain (drawn for the same underlying distribution) to accomplish a new task.Similarly, the input and task domains could differ at the same time. In these cases, 90010 domain adaptation 90011 comes to your rescue. Domain adaptation is a sub-discipline of machine learning which deals with 90010 scenarios in which a model trained on a source distribution is used in the context of a different (but related) target distribution 90011. In general, domain adaptation uses labeled data in one or more source domains to solve new tasks in a target domain. The level of relatedness between the source and target domains hereby usually determines how successful the adaptation will be.90004 90003 There are multiple approaches to domain adaptation. In 90010 «shallow» (not deep) domain adaptation 90011, two methods are commonly used: 90010 reweighing the source samples 90011 and training on the reweighed samples and trying to 90010 learn a shared space to match the distributions 90011 of the source and target datasets . While these techniques may also be applicable in the context of deep learning, the deep features learned by Deep Neural Networks (90010 DNNs 90011) usually give rise to more transferable representations (generally learning highly transferable features in the lower layers while the transferability sharply decreases in higher layers, see e.g. this paper by Donahue et al). In 90010 Deep Domain Adaptation 90011, we try to make use this property of DNNs. 90004 90003 The following summary is mostly based on this review paper by Wang et al and this review by Wilson et al. In that work, the authors distinguish between different types of domain adaptation, depending on the complexity of the task, the amount of labeled / unlabeled data available, and the difference in input feature space. 90010 They specifically define domain adaptation to be a problem in which the task space is the same and the difference is solely in input domain divergence 90011.Based on this definition, domain adaptation can be either homogeneous (the input feature spaces are the same, but have different data distributions) or heterogeneous (the feature spaces and their dimensionalities may differ). 90004 90003 Domain adaptation may also occur in one step (90010 one-step domain adaptation 90011), or through multiple steps, traversing one or more domains in the process (90010 multi-step domain adaptation 90011). In this post, we will only discuss one-step domain adaptation, as this is the most common type of domain adaptation.90004 90003 Depending on the data you have available from the target domain, domain adaptation can further be classified into 90010 supervised 90011 (you do have labeled data from the target domain, albeit an amount which is too small for training a whole model), 90010 semi-supervised 90011 (you have both labeled and unlabeled data), and 90010 unsupervised 90011 (you do not have any labeled data from the target domain). 90004 90003 How can we determine if a model trained in a source domain may be adapted to our target domain? It turns out that this question is not easy to answer and task relatedness is still a topic of active research.We can define two tasks to be similar if they use the same features to make a decision. Another possibility is to define two tasks as similar if their parameter vectors (i.e. classification boundaries) are close (see this paper by Xue et al.). On the other hand, Ben-David et al. propose that two tasks are F-related if the data for both tasks can be generated from a fixed probability distribution using a set of transformations F. 90004 90003 Despite these theoretical deliberations, in practice it is probably necessary to try domain adaptation on your own datasets to see if you can obtain some benefit for your target task by using a model from the source task.Often, task relatedness can be determined by simple reasoning, such as images from different viewing angles or different lighting conditions, or, in the medical field, images from different devices and so on. 90004 90003 There are three basic techniques for one-step domain adaptation: 90004 90051 90052 divergence-based domain adapatation, 90053 90052 adversarial-based domain adaptation using generative models (GANs) or using domain-confusion loss, and 90053 90052 reconstruction-based domain adaptation using stacked autoencoders (SAEs) or GANs.90053 90058 90059 Divergence-based Domain Adaptation 90060 90003 Divergence-based domain adaptation works by 90010 minimizing some divergence criterion between the source and target data distributions 90011, thus achieving a 90010 domain-invariant feature representation 90011. If we find such a feature representation, the classifier will be able to perform equally well on both domains. This of course assumes that such a representation exists which in turn assumes that the tasks are related in some way.90004 90003 The four most frequently used divergence measures are 90010 Maximum Mean Discrepancy 90011 (MMD), 90010 Correlation Alignment 90011 (CORAL), 90010 Contrastive Domain Discrepancy 90011 (CCD) and the Wasserstein metric. 90004 90003 MMD is a hypothesis test which tests whether two samples are from the same distribution by comparing the means of the features after mapping them to a Reproducing Kernel Hilbert Space (RKHS). If the means are different, the distributions are likely different as well.This is often accomplished by using the kernel embedding trick and comparing the samples using a Gaussian kernel. The intuition here is that if two distributions are identical, the average (mean) similarity between samples from each distribution should be equal to the average similarity between mixed samples from both distributions. An example for using MMD in domain adaptation is this paper by Rozantsev et al. In this paper, two-stream architecture is used with weights which are not shared but which lead to similar feature representations by using a combination of classification, regularization and domain discrepancy (MMD) loss, as in the figure below.90004 Two-stream architecture by Rozantsev et al. 90003 Hereby, the setting may either be supervised, semi-supervised or even unsupervised (no classification loss in target domain). 90004 90003 CORAL (link) is similar to MMD, however it tries to align the second-order statistics (correlations) of the source and target distributions instead of the means using a linear transformation. This paper by Sun et al. uses CORAL in the context of deep learning by constructing a differentiable CORAL loss using the Frobenius norm between the source and target covariance matrices.90004 90003 CCD is also based on MMD but also makes use of label distributions by looking at conditional distributions. This makes sure that the joint domain features still retain predictivity w.r.t the labels. Minimizing CCD minimizes the intra-class discrepancy while maximizing the inter-class discrepancy. This requires both source and target domain labels. In order to get rid of this constraint, Kang et al. propose to estimate missing target labels using clustering in an iterative procedure which jointly optimizes target labels and feature representations.Hereby target labels are found by clustering, whereupon CCD is minimized to adapt the features. 90004 90003 Finally, feature and label distributions in the source and target domains may be aligned by considering the optimal transport problem and its corresponding distance, the Wasserstein distance. This is proposed in DeepJDOT (Damodaran et al). The authors propose to minimize the discrepancies between joint deep feature representations and labels through optimal transport. 90004 90059 Adversarial-based Domain Adaptation 90060 90003 This technique tries to achieve domain adaptation by using adversarial training.90004 90003 One approach is to generate synthetic target data which are somehow related to the source domain (e.g. by retaining labels) using 90010 Generative Adversarial Networks 90011 (GANs). These synthetic data are then used to the train the target model. 90004 90003 The 90010 CoGAN model 90011 90010 90011 tries to achieve this by using two generator / discriminator pairs for both the source and target distributions. Some weights of the generators and the discriminator are shared to learn a domain-invariant feature space.In this way, labeled target data can be generated that can further be used in a task such as classification. 90004 CoGAN architecture by Liu et al. 90003 In another installation, Yoo et al. try to learn a 90010 source / target converter network 90011 by using two discriminators: one for ensuring that the target data are real, the other to preserve relatedness between the source and target domains. The 90010 generator 90011 is hereby 90010 conditioned on the source data 90011. This approach only requires unlabeled data in the target domain.90004 90003 We can also 90010 get rid of generators 90011 altogether and perform domain adaptation in one go if we use the so-called 90010 domain confusion loss 90011 in addition to the classification loss used for the current task. The domain confusion loss is similar to the discriminator in GANs in that it tries to match the distributions of the source and target domains in order to «confuse» the high-level classification layers. Perhaps the most famous example of such a network is the 90010 Domain-Adversarial Neural Network 90011 (DANN) by Ganin et al.This network consists of two losses, the classification loss and the domain confusion loss. It contains a 90010 gradient reversal layer 90011 to match the feature distributions. By minimizing the classification loss for the source samples and the domain confusion loss for all samples (while maximizing the domain confusion loss for the feature extraction), this makes sure that the samples are mutually indistinguishable for the classificator. 90004 Domain-adversarial neural network architecture by Ganin et al.90059 Reconstruction-based Domain Adaptation 90060 90003 This approach uses an 90010 auxiliary reconstruction task 90011 to create a shared representation for each of the domains. For instance, the 90010 Deep Reconstruction Classification Network 90011 (DRCN) tries to solve these two tasks simultaneously: (i) classification of the source data, and (ii) reconstruction of the unlabeled target data. This makes sure that the network learns not only to discriminate correctly but also preserves information about the target data.In the paper the authors also mention that the reconstruction pipeline learns to transform source images into images that resemble the target dataset, suggesting that a common representation is learned for both. 90004 The DRCN architecture by Ghifary et al. 90003 Another possibility is to use so-called 90010 cycle GANs 90011. Cycle GANs are inspired by the concept of dual learning in machine translation. This concept trains two opposite language translators (A-B, B-A) simultaneously. Feedback signals in the loop consist of the corresponding language models and the mutual BLEU scores.The same can be done with images using the im2im framework. In this paper, a mapping from one image domain to another is learned without using any paired image samples. This is accomplished by simultaneously training two GANs that generate the images in the two domains, respectively. To ensure consistency, a 90010 cycle consistency loss 90011 is introduced. This makes sure that the transformation from one domain to another and back again results in roughly the same image as the input. Thus, the full loss of the two paired networks is a sum of the GAN losses of both discriminators and the cycle consistency loss.90004 90003 Finally, GANs can also be used in an encoder-decoder setting by conditioning their input on the image from another domain. In the paper by Isola et al., 90010 conditional GANs 90011 are used to translate images from one domain to another by conditioning the discriminator and the output of the generator on the input. This can be achieved using a simple encoder-decoder architecture or alternatively using a U-Net architecture with skip-connections. 90004 Several results from the conditional GAN ​​by Isola et al.90003 Deep domain adaptation allows us to transfer the knowledge learned by a particular DNN on a source task to a new related target task. It has been successfully applied in tasks such as image classification or style transfer. In some sense, deep domain adaptation enables us to get closer to human-level performance in terms of the amount of training data required for a particular new computer vision task. Therefore, I think that progress in this area will be crucial to the entire field of Computer Vision and I hope that it will eventually lead us to effective and simple knowledge reuse across visual tasks.90004.90000 What are 10 rattlesnake adaptations? 90001 90002 90003 90004 90005 Biology 90006 Science 90007 90002 90003 Anatomy & Physiology 90004 90003 Astronomy 90004 90003 Astrophysics 90004 90003 Biology 90004 90003 Chemistry 90004 90003 Earth Science 90004 90003 Environmental Science 90004 90003 Organic Chemistry 90004 90003 Physics 90004 90005 90006 Math 90007 90002 90003 Algebra 90004 90003 Calculus 90004 90003 Geometry 90004 90003 Prealgebra 90004 90003 Precalculus 90004 90003 Stat 90004 90005.