优化自然角提升幼儿探究性学习的有效性

自然角将大自然引入幼儿生活，弥补了幼儿活动时间和空间的局限，是幼儿身边的实验室和深入探究的场所。本文就目前自然角创设中存在的问题，分析其不利于幼儿探究学习的原因。在实践研究的基础上，从遵循幼儿特点、感知幼儿需要、追随幼儿探究过程出发，提出创设适宜的、支持性的、动态性的探究环境的策略，以帮助幼儿真正在自然角的探究中获得发展。

基于多元智能理论的学业评价观研究

基于加德纳"多元智能理论"中不同的智能类型,我们可以有针对性地采用八种智能评价方法来改进学业评价观。然而,传统的应试化学业评价观在评价目的、评价内容、评价主体及评价方法等方面与多元智能理论均有所背离,对此,我们可以从"设立判断学生智能类型的学习角"、"创建专题作业形式的研究性学习"、"构建情境化的质性评价"三个维度来改进学业评价观。

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

浅议主题背景下学习性区角现状

幼儿园也称幼稚园,当前幼稚园教学,学习性区角是其广泛推行的一种授课方式,它包含的内容多样,能够营造轻松愉快的氛围,它是孩子们主动选择,主动学习,主动探究,主动模仿,主动开展的活动之一。学习性区角是儿童认知与学习的区间与区域,即学习角。在实践中不难看到:学习角既注重在不同年龄阶段孩子们的成长经历,也更加注重每个孩子逐步的,阶段性的发展与进步。如何组织与指导幼儿的学习角是当前最关注的问题。为了达到更好实践的效果,从内容、材料、指导这三方面的观察与研究,对学习角的组织与指导有了新的尝试。

例谈幼儿艺术经验建构

艺术允许幼儿想象,解决问题,表达他们的想法和情感,使他们的体验更有意义。经验对于幼儿来说,不仅是学习的基础,更重要的是经历、行动和过程,是伴随着交互作用不断生成、动态发展的。课程游戏化实施以来,幼儿园结合“艺术学习角”的创设,让幼儿在真实的情境中通过直接感知、亲身体验和动手操作,不断丰富艺术体验,在循环往复的经验建构过程中丰富艺术认知,从而促进幼儿艺术经验、能力、素养的提升,为终身艺术发展奠定基础。

Incremental learning of the triangular membership functions based on single-pass FCM and CHC genetic model

In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs. The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs. The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs. Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM. The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach. The quality of TMFs generated by the approach is comparable to that of the batch learning approach. Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.

谈开展幼儿自主性区域活动的有效策略

区域游戏在今天并不是一个新鲜的话题,然而在幼儿一日生活中却占据了越来越重要的位置,它作为一种开放的、灵活的活动方式,已经成为幼儿自主学习、自我成长的重要载体之一。使我们更加深切的感受到它对幼儿发展的独特魅力。近些年来,新的教育目标价值观要求我们要更加关注幼儿的自主性学习,能够充分展现幼儿自主性学习的学习性区角活动提出与实践,也引发了幼儿园广泛的重视和研究。

Research on the Causes and Coping Strategies of the Land Subsidence Caused by the Tunnel Construction Projects

In this paper, we conduct research on the causes and coping strategies of the land subsidence caused by the tunnel construction projects. We analyze the issues from the following of the perspectives. （1） Analysis method. To solve large scale system of the development of computer hardware and the numerical calculation method, we use the basic analysis to deal with it. （2） The empirical of methods. Ground motion is usually leads to the basic development of the inclined tunnel surface vertical displacement, the result of the movement process can turn to a settling tank. （3） Machine learning based approaches. In one of biggest difficulties when using neural network method is to obtain all possible parameters related to ground subsidence, we use the machine learning model to handle the challenge. In the final part, we show prospect for the future research, we will combine more numerical analysis tools to optimize the current methodology.

LEARNING STRATEGY TRAINING——A CRUTCH FOR LANGUAGE LEARNERS

Successful FL learners are characterized by knowing how to use language learning strategies effectively. This pilot study demonstrates the procedure to cultivate the effectiveness of the strategy training to the learners of low language proficiency. It begins with a questionnaire surveying the learners' use of strategy in language acquisition, and then a strategy training integrated with the class teaching and learners' learning diary are introduced. The analysis proves that strategy training to learners of low proficiency can become a crutch for their further study.The role of teachers in the process of the training is also discussed. The author proposes that teachers should be ready for the shift of the role to be an organizer, facilitator, councilor or cooperator, even a negotiator in the road of promoting autonomous learners.

Robust object tracking with RGBD-based sparse learning

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.

View-invariant human action recognition via robust locally adaptive multi-view learning

Human action recognition is currently one of the most active research areas in computer vision. It has been widely used in many applications, such as intelligent surveillance, perceptual interface, and content-based video retrieval. However, some extrinsic factors are barriers for the development of action recognition; e.g., human actions may be observed from arbitrary camera viewpoints in realistic scene. Thus, view-invariant analysis becomes important for action recognition algorithms, and a number of researchers have paid much attention to this issue. In this paper, we present a multi-view learning approach to recognize human actions from different views. As most existing multi-view learning algorithms often suffer from the problem of lacking data adaptiveness in the nearest neighborhood graph construction procedure, a robust locally adaptive multi-view learning algorithm based on learning multiple local L 1-graphs is proposed. Moreover, an efficient iterative optimization method is proposed to solve the proposed objective function. Experiments on three public view-invariant action recognition datasets, i.e., ViHASi, IXMAS, and WVU, demonstrate data adaptiveness, effectiveness, and efficiency of our algorithm. More importantly, when the feature dimension is correctly selected （i.e., 〉60）, the proposed algorithm stably outperforms state-of-the-art counterparts and obtains about 6% improvement in recognition accuracy on the three datasets.

Disaster Risk Reduction Efforts in the Greater Horn of Africa

This article assesses the current state of disaster risk reduction(DRR) in the Greater Horn of Africa(GHA),and focuses on interventions and policies to mitigate hydrometeorological risks. The research analyzes, as main case study, the program 'Regional Climate Prediction and Risk Reduction in the Greater Horn of Africa'funded by the Office of U.S. Foreign Disaster Assistance(USAID OFDA) in the early 2000 that targeted risk preparedness.The research method combines a desk review of relevant documents and research papers with surveys and interviews directed to key proponents of DRR across the GHA. Results highlight current strengths and weaknesses in the way DRR is implemented in the GHA. Significant improvements in the climate-forecasting capabilities in the GHA since the 2000 s are acknowledged, but the practice of DRR remains technology driven and impacts on the ground are limited. The key findings highlight the significant communication gaps that exist between the producers of climate information and their end users, the communities at risk. The article urges the establishment of bridges that connect climate experts, policymakers, and representatives of the local communities, and for the implementation of a feedback loop from forecast users to their producers, in order to strengthen risk resilience across the GHA.