基于文献关键词统计分析的我国开放与远程教育研究现状探析

以中国知网（CNKI）为平台,利用共词分析方法,对2006年1月至2015年6月间公开发表在开放与远程教育领域期刊论文的关键词进行统计分析,结合SATI软件和UCINET软件进行数据处理,探讨高频关键词之间的内在联系,揭示我国开放与远程教育研究的主题结构和核心领域。结果表明：此间我国开放与远程教育领域主要存在七大研究主题;远程学习、网络课程、学习支持服务、学习者是该领域的研究核心;移动学习、内容分析、学习科学和远程学习者将成为未来的研究热点。     

中国城市化进程效率估算及影响因素分析

本文基于2008—2012年中国285个地级市的数据,运用双边随机前沿模型测算了中国城市化进程的前沿水平以及实际水平相对前沿水平的偏离程度。结果发现,下偏效应大于上偏效应,即中国城市化进程整体表现为滞后,并且发现投资作用和政府作用在影响城市化进程中的表现为负向,另外城市化进程也表现出地区间的不平衡。最后根据本文的发现,提出相应的建议。     

县域经济竞争力的评价与分析

县域经济综合竞争力评价的对象是县域社会经济发展的整体状况,评价方法是综合性的,评价必须准确可靠,兼顾适用性和动态性.评价指标体系应遵循完备性、可比性、代表性、可行性等原则,选取能反映县域经济发展水平、发展活力和发展潜力的指标.采用综合因子分析方法,对安徽省2004年各县域经济27个指标进行评价分析,发现安徽省县域经济竞争力整体偏弱.     

中国旅游服务贸易竞争力的影响因素分析

改革开放以来,我国旅游服务贸易保持了快速、健康的发展,中国旅游服务贸易竞争力越来越强,但与世界旅游强国相比仍有很大差距.实证分析的结果表明:我国旅行社数量对提高旅游服务贸易竞争力有促进作用.旅游交通对我国旅游出口收入有显著影响,其影响力要大于旅游产业组织的影响.我国的旅游人力资源状况对于旅游服务贸易竞争力的影响不显著.     

充分发挥家庭因素在大学生就业过程中的作用

大学生就业竞争日趋激烈,影响大学生就业过程的因素很多,其中家庭因素对大学生就业能力的塑造不可忽视,家庭因素对大学生就业质量、就业观念、就业行为有较大影响当前应合理利用家庭因素促进大学生就业.     

由情感因素看教师角色的转换

情感因素在现代教育特别是现代外语教育中的地位越来越突出.积极情感可显著提升教学质量,积极情感的养成需要教师对其角色进行调整.具体来讲,教师要通过尊重学生的个性化选择、以诚相待和培养学生自信心等方法,实现自身由"讲师"向"导师"的角色转换.     

高职学生英语学习内部因素调查分析及对策研究

影响高职学生英语学习绩效存在着多种因素,教师只有了解和掌握学习者影响学习效果的因素才能有的放矢地搞好教学。对北京联合大学高职学生在英语学习中的学习动机、学习态度、学习能力和学习方法等4项内部学习因素进行了调查,分析调查结果及其生成的原因,并提出了一些相应的教学策略。     

Bayesian t-tests for correlations and partial correlations

In this paper, we develop Bayes factor based testing procedures for the presence of a correlation or a partial correlation. The proposed Bayesian tests are obtained by restricting the class of the alternative hypotheses to maximize the probability of rejecting the null hypothesis when the Bayes factor is larger than a specified threshold. It turns out that they depend simply on the frequentist t-statistics with the associated critical values and can thus be easily calculated by using a spreadsheet in Excel and in fact by just adding one more step after one has performed the frequentist correlation tests. In addition, they are able to yield an identical decision with the frequentist paradigm, provided that the evidence threshold of the Bayesian tests is determined by the significance level of the frequentist paradigm. We illustrate the performance of the proposed procedures through simulated and real-data examples.     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.     

Bayesian testing of restrictions on vector autoregressive models

In this study, we propose a prior on restricted Vector Autoregressive (VAR) models. The prior setting permits efficient Markov Chain Monte Carlo (MCMC) sampling from the posterior of the VAR parameters and estimation of the Bayes factor. Numerical simulations show that when the sample size is small, the Bayes factor is more effective in selecting the correct model than the commonly used Schwarz criterion. We conduct Bayesian hypothesis testing of VAR models on the macroeconomic, state-, and sector-specific effects of employment growth.