基于远程指导和角色扮演理论视野下的考核方式改革研究——以创新教学课程实操考核为例

角色扮演是人们实现角色社会功能的一种方式,而将这种方式引入到课程考核模式进行研究是一个重要突破。基于网络课程的远程学习与指导,形成有机联系的模块化教学,推动创新教学课程角色扮演实操考核的实践,把创新性教学理论与实践创新性教学有机结合,提升学生把创新理论向教学创新实践转化的能力,对学生面向学校教学改革实际的角色认同和角色改进具有现实的和深远的意义。     

网络教育中引领式毕业设计系统的设计

网络教育试点10年以来,作为重要的实践教学环节的毕业论文（设计）逐渐迁移到了网络平台上。互联网上开展毕业论文（设计）既要考虑其流程的合理性,更重要的是要采取多种手段引导学生完成毕业论文（设计）的任务。为此结合调查数据,通过分析各环节上学生支持的要点,设计了基于引领模式的毕业论文（设计）系统,系统引领学生在特定时间、有计划地分任务逐步完成毕业设计相关环节。     

构建和谐师生关系是师德建设的基础工程

师德集中体现在师生关系上,师生关系与师德建设在本质上具有一致性。然而,现代大学教育中师生关系的疏远与隔膜成为一个突出的问题,师生关系的＂职业性＂和世俗化导致教育效果不佳,直接影响到了师德建设。所以,构建新型和谐的师生关系,成为师德建设的基础工程。本文坚持以生为本理念,从教师素养、民主平等、道德规范、大学文化建设等方面提出师德建设的策略。     

On the inapproximability of the exemplar conserved interval distance problem of genomes

In this paper we present two main results about the inapproximability of the exemplar conserved interval distance problem of genomes. First, we prove that it is NP-complete to decide whether the exemplar conserved interval distance between any two genomes is zero or not. This result implies that the exemplar conserved interval distance problem does not admit any approximation in polynomial time, unless P=NP. In fact, this result holds, even when every gene appears in each of the given genomes at most three times. Second, we strengthen the first result under a weaker definition of approximation, called weak approximation. We show that the exemplar conserved interval distance problem does not admit any weak approximation within a super-linear factor of , where m is the maximal length of the given genomes. We also investigate polynomial time algorithms for solving the exemplar conserved interval distance problem when certain constrains are given. We prove that the zero exemplar conserved interval distance problem of two genomes is decidable in polynomial time when one genome is O(log n)-spanned. We also prove that one can solve the constant-sized exemplar conserved interval distance problem in polynomial time, provided that one genome is trivial.     

Natural (Non‐)Informative Priors for Skew‐symmetric Distributions

In this paper, we present an innovative method for constructing proper priors for the skewness (shape) parameter in the skew‐symmetric family of distributions. The proposed method is based on assigning a prior distribution on the perturbation effect of the shape parameter, which is quantified in terms of the total variation distance. We discuss strategies to translate prior beliefs about the asymmetry of the data into an informative prior distribution of this class. We show via a Monte Carlo simulation study that our non‐informative priors induce posterior distributions with good frequentist properties, similar to those of the Jeffreys prior. Our informative priors yield better results than their competitors from the literature. We also propose a scale‐invariant and location‐invariant prior structure for models with unknown location and scale parameters and provide sufficient conditions for the propriety of the corresponding posterior distribution. Illustrative examples are presented using simulated and real data.     

Profiles identification on hierarchical tree structure data sets

In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis–Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.     

Optimal Berry–Esseen bound for parameter estimation of SPDE with small noise

We investigate a rate of convergence on asymptotic normality of the maximum likelihood estimator (MLE) for parameter $\theta$ appearing in parabolic SPDEs of the form

$d{u}^{?}(t,x)=(A_0+\theta A_1)u^{?}(t,x)dt+?dW(t,x),$

where $A_0$ and$A_1$ are partial differential operators, $W$ is a cylindrical Brownian motion (CBM) and $?\downarrow 0$. We find an optimal Berry–Esseen bound for central limit theorem (CLT) of the MLE. It is proved by developing techniques based on combining Malliavin calculus and Stein’s method.     

三元效率区间下决策单元的全局绩效评价

针对现实生活中投入产出数据的不确定性,许多学者提出从乐观和悲观角度计算决策单元的效率区间,但每个效率区间的上、下界值都是决策单元表现的两种极端情况。本文通过引入心态指标衡量决策者的偏好,获得了一个最有可能的效率值,它与上界值、下界值共同组成了三元效率区间。然后改进了两级排序方法:提出了三元有向距离指数,为所有决策单元获得全序化结果。本文引用前人文中的数例验证了该方法是一种更为精确、可全序化的评价、决策方法,可广泛应用于效率测评中。     

A new per-field classification method using mixture discriminant analysis

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.