构建开放式培训体系，服务地方性经济发展

高职院校必须要与地方行业、企业紧密契合,有效利用自身,和行业、企业之间建立开放式、立体化呼应体系。高职院校既要推动自身科技创新,在先进工艺、先进技术等方面为区域经建建设提供支持,又要加强对外合作与交流,成为区域经经济建设的试验田和孵化器。     

高职畜牧兽医专业“校企互动、岗位引领”实践教学模式的探索

围绕专业人才培养目标和企业岗位能力要求,校企共同设计专业实践教学体系,共同实施以岗位任务为引领的实践教学模式,突出了高职畜牧兽医专业学生职业能力的培养.     

Norming of Student Evaluations of Instruction: Impact of Noninstructional Factors

Student Evaluations of Instruction (SEIs) from about 6,000 sections over 4 years representing over 100,000 students at the college of business at a large public university are analyzed, to study the impact of noninstructional factors on student ratings. Administrative factors like semester, time of day, location, and instructor attributes like gender and rank are studied. The combined impact of all the noninstructional factors studied is statistically significant. Our study has practical implications for administrators who use SEIs to evaluate faculty performance. SEI scores reflect some inherent biases due to noninstructional factors. Appropriate norming procedures can compensate for such biases, ensuring fair evaluations.     

基于移动互联网应用的高职计算机专业课程体系探讨

以移动互联网应用就业岗位人才需求为出发点,分析计算机专业与移动互联网应用岗位的专业契合度,探讨高职院校计算机应用专业人才培养方案及课程体系。     

认知训练在罚定位球教学中的应用

在本科足球专选班罚定位球数学中，将２４名学生分成实验组与对照组，实验组采用认知训练教学法，对照组采用常规教学法。结果表明，认知训练能够调动学生的积极思维，促进对技术动作的认知和掌握，提高罚球命中率，提高教学质量。     

用ASP.NET实现排名功能

主要探讨排名算法的原理及在实际应用中的实现。通过区分"田赛"和"径赛"的升降序,实现对比赛成绩的类似于Excel的Rank排名,并与裁判所提交的赛场手工排名进行比较,来判断裁判所提交的纸质数据是否有效,从而具有一定的纠错功能。     

企业员工隐性知识交流能力评价模型

为评价企业员工隐性知识交流能力,提出隐性知识交流能力评价模型。首先,通过问卷调查获得企业员工隐性知识流转情况,并据此建立隐性知识流转网络。其次,根据隐性知识流转网络以及Page Rank算法的基本思想建立隐性知识交流能力评价模型。在该模型中,能经常与别人交流的员工,交流能力强;与交流能力强的人交流,自身的交流能力也会提升;每个人用于知识交流的精力是有限的;每个人不会把所有的隐性知识分享给别人。最后讨论分析模型的解。     

论加强中专学校图书馆干部队伍建设

文章论述了中专学校图书馆干部队伍现状和问题。提出要转变观念、吸引人才,并留住人才、用好人才,从根本上改善干部队伍结构。参考文献2。     

1993年全国各省（市）高校科研综合实力评价及排序

本文采用加权秩和比的评价方法,对1993年全国各省(市)高校的科研综合实力进行了评价和排序,为定量评价科研的综合实力和效益提供一种新的方法.     

Automatic ranking of information retrieval systems using data fusion

Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods.