Writing Revision: Evidence for Learning

ABSTRACT

This paper identifies evidence that significant procedural learning can emerge from process approaches to teaching writing, including from the transition of pupils’ writing from draft to revision. It shows how writing schemes that use an underlying process framework to structure learning, give pupils ownership of their own writing and exploit the resources of classes as learning communities can enable pupils to learn how to write more effectively, and in some cases to discover what they want to say through the process of composition. The paper draws on this and other work to set out a number of implications for teaching and classroom practice.     

推理链：科学发现的逻辑新论

科学发现有没有逻辑的争论,由来已久.有人称其无,有人说其有,还有归纳派和演绎派的形同水火.究其实,争论是由逻辑观--传统形式逻辑和现代数理逻辑的不同引起的.其实,归纳派和演绎派各执一端,各有长短,彼此是相通的,类比、归纳和演绎整合的推理链,才是科学发现的逻辑.     

广义双向S-粗集和它的一般结构

基于双向S-粗集和一般二元关系吼，提出广义双向S-粗集，对广义双向S-粗集的一般结构进行了描述。对广义双向S-粗集的有关性质进行了讨论。并给出了具体实例和意义解释。广义双向S-粗集为研究一般系统动态的近似特性提供了新的途径和方法。     

Supporting learning with interactive multimedia through active integration of representations

When learners explore dynamic and interactive visualisations they are often not able to interact with them in a systematic and goal-oriented way. Frequently, even supporting learners in processes of discovery learning does not lead to better learning outcomes. This can be due to missing pre-requisite knowledge such as the coherent mental integration of the pictorial and symbolic sources of information. In order to support learners in this process, we encouraged them to interactively and externally relate different static sources of information to each other before exploring dynamic and interactive visualisations. We evaluated the benefit of this instructional support in two experimental studies concerning the domains of statistics and mechanics. It revealed that the active integration of static representations before processing dynamic visualisations resulted in better performance and can provide a basis for a more systematic and goal-oriented experimentation behaviour during simulation-based discovery learning.in final form: 16 November 2004     

发现学习与研究性学习探析

作为研究性学习基础的发现学习和作为发现学习提升与发展的研究性学习，在具体的实施中对其二者内涵的深度把握十分重要。本文从发现学习和研究性学习的理论基础、特征及目标定位等方面分析两者的异同，以为基础教育课程改革献微薄之力。     

特定用户群体关系挖掘与分析研究

为挖掘当前社交网络中具有相同内在因素、特定组织结构的群体,提出一种基于特定用户的群体关系挖掘与分析方法。首先,以特定用户为切入点,改进基于最短路径的图聚类算法,以此关联它们之间的关系,构建初级群体;然后,构造初级群体属性特征比对集合,利用动态权重相似性对其进行扩展,挖掘它们所处的群体;最后,对挖掘出的群体进行聚类效果评估。实验结果表明,该方法聚类效果良好,能够有效挖掘相关群体,为社交网络中的群体发现提供了新思路。     

基于网络的文献知识发现系统研究

本文分析了对网络文献知识发现系统的基本要求，介绍了知识发现系统的一般结构，提出用移动多智能系统构建基于网络的文献知识发现系统的观点，讨论了有关网络文献知识发现系统的评价方法。     

从人的发展角度对接受学习与发现学习的思考

学习方式影响人的发展,但不决定人的发展。接受学习和发现学习是两种主要的学习方式。因为在现阶段接受学习比发现学习有明显的比较效率,所以在实际的教学中接受学习被更广泛地使用。采用什么样的学习方式受许多条件制约。     

基于样本加权的引文网络的社团划分

[目的/意义] 为提高引文网络的社团划分的准确性,提出一种基于加权的引文网络的社团划分方法。[方法/过程] 以Louvain社团划分方法为算法基础,将科学论文用向量空间模型表示,利用改进的余弦相似度方法计算相邻论文之间的相似度,并将其作为权重,综合考虑论文内容属性与结构属性,提出一种基于样本加权的引文网络社团划分方法。[结果/结论] 该算法将引文网络中论文的文本内容属性与拓扑结构属性结合起来,通过对Scientometrics期刊发表的论文以及主题为CRISPR的论文进行社团划分研究实验,结果表明该方法能改善引文网络社团的划分效果。     

The Deep-Time Digital Earth program: data-driven discovery in geosciences

Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth''s evolution.