国家体制中的民族管理制度类型及其成因

本文在界定民族管理制度基础上,按其功能将民族管理制度分类为:以生态生计管理为基础形成的生产及保障为一体的管理制度;以杜绝或禁止人与自然、人与人关系中的负面行为为主的禁忌制度;以彰显民族凝聚力为目标的鼓励性仪式制度。其成因是:不同生态环境是每个民族形成不同管理制度的基础;在不同生态环境基础上创造的生计方式是民族管理制度建立的雏形;民族作为群体是民族管理制度形成和存在的载体;民族文化进一步巩固和强化了民族管理制度。     

试论休谟哲学思想的源流及价值（上）

作为启蒙时代产物的休谟哲学通过对人类知识起源的考察,提出不可知论的观点,休谟把因果关系看作心理习惯的产物,否定了因果关系的客观性和必然性．对宗教的起源扣各种证明进行了批判性的考察．坚持情感主义道德观,他以批判的精神开创了西方哲学史上第一个不可知论的哲学体系,对康德哲学和现代西方哲学产生了深刻的影响,其思想中的怀疑思维和批判精神具有一定的现实意义和当代价值．     

英汉语说明性因果关系表达法对比

本从句法、语义和语用三个平面，对英汉语说明性因果关系表达法作一对比，发现显性表达法既有交叉相似部分，又有很大不同，句子层次上的隐性表达法彼此相差甚远。这些区别从一个侧面反映了英语的“形合”和汉语的“意合”特点。     

The Importance of Forgetting ‘Perishing’ in Whitehead’s Cosmology and Epistemology

In this paper, I will address a constructive criticism of the papers that appeared as a Symposium on Whitehead’s Process Philosophy of Education (Interchange, 26(4), pp. 341–415, 1995). In his criticism of those papers, George Allan (1998) claimed that the contributors to the Symposium were not as Whiteheadian as they thought they were because they failed to contextualize their papers in the harmony and holism of Whitehead’s organic philosophy. Allan thought, in essence, that we had committed a fallacy of misplaced concreteness. Ironically, this is exactly what we had been trying to avoid. With regards to my paper, Allan felt that I had failed to explain the importance of perishing for Whitehead in the becoming and objective immortality of the superject of experience. Hopefully, I have done a better job of this in what follows. I would also like to begin exploring the implications of Whitehead’s cosmology and epistemology for the advance of theory in psychology. On a pleasant afternoon in Oulu, Finland I was discussing the ideas presented here with my friend Hannu Soini who said to me, “when one is learning it is important to forget certain things so they do not impede our creativity.” Hence, the title of this paper and a further exploration of the concepts that Hannu and I feel are impeding the advance of psychology. I would like to preface with a reminder that when I try to explain Whitehead’s philosophy succinctly I sometimes fail to explain things well. His is a truly organic conception of reality so please forgive me.     

详析模式方法及其在教育科学研究中的应用

详析模式是一种因果分析的多变量检验模型,通过引入检验变量来辨别、阐明和揭示原变量问的真实性因果关系。在教育研究中运用详析模式方法,能够检验教育变量间因果关系的真假、揭示变量间相互关系的县体条件、挖掘教育变量间的潜在关系,从而有效避免由对教育调查资料的感性认识而得出错误结论,有助于抓住教育问题的实质,为正确地进行教育决策提供切实可靠的依据。     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

     

Causal Inference and COVID: Contrasting Methods for Evaluating Pandemic Impacts Using State Assessments

In the spring of 2021, just 1 year after schools were forced to close for COVID-19, state assessments were administered at great expense to provide data about impacts of the pandemic on student learning and to help target resources where they were most needed. Using state assessment data from Colorado, this article describes the biggest threats to making valid inferences about student learning to study pandemic impacts using state assessment data: measurement artifacts affecting the comparability of scores, secular trends, and changes in the tested population. The article compares three statistical approaches (the Fair Trend, baseline student growth percentiles, and multiple regression with demographic covariates) that can support more valid inferences about student learning during the pandemic and in other scenarios in which the tested population changes over time. All three approaches lead to similar inferences about statewide student performance but can lead to very different inferences about student subgroups. Results show that controlling statistically for prepandemic demographic differences can reverse the conclusions about groups most affected by the pandemic and decisions about prioritizing resources.