终身教育与学生自主学习能力的培养

自主学习能力对于学习者而言是一项重要技能,是实现终身教育所必备。本文分析了自主学习能力的培养对于实现有效终身教育的必要性,探讨了在现代信息技术日益改变着教学模式的背景下如何培养学生的自主学习能力。笔者认为,树立以学生为中心的教学理念是实现自主学习能力培养的关键,培养学习动机、学习策略以及信息素养是提高自主学习能力的三个重点。     

高校创业教育教学途径初探

随着创业教育新课程实践而产生的新教学方法,既与传统教育方法有一定联系,又与它有很大的区别。新教学方法的本质在于引导学生主动地参与学习活动,使学生在知识能力、情感态度、创新等方面都得到主动发展。文章从分阶段培养、项目化运作、基地化实施、市场导向等几个方面探讨了创业教育教学的新方法。创业教育的教学导向在于加强基础知识与核心能力的培养,重在联系实际,注重知识的生活应用,以体现学以致用的原则。     

概念迁移视阈下基于语料库的非英语专业学习者动名词搭配错误研究

文章拟从概念迁移视角研究大学英语写作中的动-名词搭配错误,研究采用语料库定量与定性相结合的分析方法。语料取材于中国学习者英语语料库（CLEC）,包括大学英语低年级学习者（St3）和高年级学习者（St4）两个子语料库。利用检索工具Ant Conc3.58检索中国学习者作文中的动-名词搭配错误,从而对大学生英语写作中的动-名词搭配错误进行描述与分类,进而分析造成大学生英语写作动-名词搭配误用的潜在原因,并对现有大学英语教学做出反思。     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.     

A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring

We propose an approach to determine the distribution of particular linear combinations of hybrid censored order statistics which is based on the calculation of volumes of polytopes. For this purpose, we establish efficient and compact volume formulas in terms of B-splines. Further, we illustrate our approach for ten different progressive hybrid censoring schemes under an exponential assumption.     

The emergence of the emergence-based approach in community practice

Community practitioners are often challenged to work in complex environments and situations that naturally occur in communities. This article focuses on the theoretical and practical use of the Emergence-Based Approach that is relevant for work in complex community situations. Using this approach, the practitioner encourages the emergence of novel ideas and initiatives, and engages a decentralized network of activists, helping them develop their initiatives. Using a field study, we compare this approach to the traditional Outcomes-Based Approach. Although their mindsets are very different, and even contradict one another, we suggest practical ways to combine the two in an effective way.     

思想史视野中的质性研究:以方法意涵的构建为例

本文尝试从思想史视角反思质性研究方法,以揭示被结构视角遮蔽的研究过程,从而探讨一种直面内心的反思性社会科学研究方式。思想史视角强调回到文本去阐释书写者赋予研究方法以意涵的思想过程。本文以“推论”“充分必要性”与“意义”等三种方法论立场中的方法意涵构建过程为例展现这样的思想过程：方法论如何引导了对特定研究方法的选择;当方法论与现实情况存在差距时,书写者如何在原有方法论立场中发展对质性方法新的理解以捍卫方法论的合理性;书写者如何在强调与其他立场的区分中发展对方法的理解。本文由此揭示了看似相同的方法背后所蕴含的巨大意涵和实践差异。一种围绕“意涵”展开的方法知识系统将有可能被建立起来。思想史视角为反思性的质性方法知识构建提供了可以付诸实践的路径。