图书馆学研究多层次性发展：历程与思考

文章认为图书馆学多层次性发展是图书馆学逐渐成熟的表现,回顾图书馆学层次性发展时,注意到上世纪八十年代的图书馆学研究重视“理论联系实际”,探寻“理论与实践的结合部;而在21世纪初图书馆学研究中则强调理论的层次性,强调基础理论与应用理论的分离和独立发展,从而促进了应用图书馆学的大发展。文章对图书馆学多层次性发展作了多视角探讨,并指出应注意的问题。     

文本知识分析中的概念层次网络方法

文中提出了一种文本知识分析中的概念层次网络方法。该方法是一种文本概念分析的新思路。探讨了用面向对象技术与框架技术结合实现文本知识的抽象概念和具体概念的自动分析、知识组织、检索智能化方法。     

新中国70年高等教育层次结构变革的回顾与反思

高等教育层次结构是不同程度和要求的高等教育的相互关系和构成状态。新中国成立70年来，我国高等教育层次结构变革经历了以本科教育为主的“橄榄球型”结构、高等教育层次愈发失衡、以两端为重点的高等教育层次结构恢复与升级、层层递进的“金字塔型”结构、高等教育层次内涵式发展的演进阶段。面向未来，高等教育层次结构的变革应着眼于质量提升、层次衔接、结构优化的目的，从构建成熟的“谷仓型”结构、创新各层次办学模式、建设“双一流”重点高校和培养应用型人才等方面入手，不断优化高等教育层次结构。     

论用HNC理论进行中文信息处理与知识处理

中文信息的处理要达到相当程度的自动化,必须让计算机具有智能化,必须解决计算机理解语言的问题,实现信息处理向知识处理转换的现代化,HNC(概念层次网络)理论,通过建立一个模拟人类感知过程的理论模式为实现中文信息处理向知识处理转换创造了条件。     

河北省十一个地级市经济聚类分析

本文采用系统聚类的方法，将河北省11个地级市按照经济指标的相似程度进行聚类，划分为三个经济梯度区，通过分析区域差异的空间特征，提出了不同等级区域城市发展的对策。     

中学生课外补习时间投入对学业成绩的影响——基于PISA2015我国四省市数据的实证研究

基于PISA2015中国四省市数据,采用多层线性模型,研究中学生各科课外补习时间投入对学业成绩的影响效应.研究发现:课外补习时间与学业成绩之间存在先降后升的非线性关系,表明学科补习具有门槛效应,当补习时间超出某个阈值,学业成绩才会出现质的提升;数学补习时间投入差异扩大了不同家庭背景学生间数学成绩的差距,会造成教育结果的不均等;校内课程时间对学生成绩的影响呈先升后降的非线性关系,相较于补习时间,校内课程时间能更有效地提升学生成绩.因此,中学生及家长应基于实际的补习需求,合理投入补习时间;学校应保证学生校内课程学习时间,发挥学校教育的主导作用;政府应重视课外补习对教育公平的冲击,加强对校外培训机构的监管,还要为经济困难家庭学生提供必要的课外补习机会,缩小因课外补习带来的教育结果不均等,努力促进校内外教育公平.     

Role of teacher-related factors and educational resources in science literacy: An international perspective

The study evaluated the relationship among teaching, leadership, teacher certification, student and teacher number related factors, and index of economic, social and cultural status (ESCS), and science literacy from an international comparative perspective. 2015 dataset of the Programme for International Student Assessment for Turkey, Singapore, the United States, Korea, Italy, and Brazil was used. Two-level hierarchical linear models were established for analysis. Results revealed that school ESCS was the best predictor of science literacy across six countries and additionally teacher-related factors came to the forefront of science literacy performance. The study underlined policymakers should focus primarily on policies that increase equity in education. Furthermore, the effectiveness of investments made in factors related to leadership, teacher certification, the number of students and teachers, which contribute very little to science literacy performance, need to be more questioned. Inferences about the findings were discussed in detail, and recommendations for further research were made.     

Improving unsupervised keyphrase extraction by modeling hierarchical multi-granularity features

Existing unsupervised keyphrase extraction methods typically emphasize the importance of the candidate keyphrase itself, ignoring other important factors such as the influence of uninformative sentences. We hypothesize that the salient sentences of a document are particularly important as they are most likely to contain keyphrases, especially for long documents. To our knowledge, our work is the first attempt to exploit sentence salience for unsupervised keyphrase extraction by modeling hierarchical multi-granularity features. Specifically, we propose a novel position-aware graph-based unsupervised keyphrase extraction model, which includes two model variants. The pipeline model first extracts salient sentences from the document, followed by keyphrase extraction from the extracted salient sentences. In contrast to the pipeline model which models multi-granularity features in a two-stage paradigm, the joint model accounts for both sentence and phrase representations of the source document simultaneously via hierarchical graphs. Concretely, the sentence nodes are introduced as an inductive bias, injecting sentence-level information for determining the importance of candidate keyphrases. We compare our model against strong baselines on three benchmark datasets including Inspec, DUC 2001, and SemEval 2010. Experimental results show that the simple pipeline-based approach achieves promising results, indicating that keyphrase extraction task benefits from the salient sentence extraction task. The joint model, which mitigates the potential accumulated error of the pipeline model, gives the best performance and achieves new state-of-the-art results while generalizing better on data from different domains and with different lengths. In particular, for the SemEval 2010 dataset consisting of long documents, our joint model outperforms the strongest baseline UKERank by 3.48%, 3.69% and 4.84% in terms of F1@5, F1@10 and F1@15, respectively. We also conduct qualitative experiments to validate the effectiveness of our model components.     

运用系统聚类法对高校学生进行分类

学生管理是学校教育的重要组成部分,清晰认识学生自身特点是对学生实施有针对性管理的前提。可以运用系统聚类法中的最短距离法,按学生的思想品德、学习成绩、身心健康三项指标将学生分成四类,即优、良、中、一般,从而为学生管理工作者根据分类结果对不同类型的学生实施有针对性的管理提供科学依据。     

Effects of Exposure to Part-time Faculty on Community College Transfer

Over the past several decades, one of the most significant changes in the delivery of postsecondary education involves the dramatic increase in the use of contingent or part-time faculty. Although the increased use of part-time faculty within higher education makes sense from an administrative point of view, its use does not come without criticism. With community colleges representing a more convenient, affordable, and flexible educational option for a number of students, particularly those from disadvantaged backgrounds, examining how exposure to part-time faculty relates to students’ academic goals represents an important area of inquiry. This study draws from social and human capital frameworks and uses hierarchical generalized linear modeling (HGLM) to examine how exposure to part-time faculty relates to community college students’ likelihood of transferring to a four-year college or university. Findings suggest that students tend to be significantly less likely to transfer as their exposure to part-time faculty increases.