Two Thousand Students,One Librarian: Balancing Depth and Breadth of Library Instruction for Online Graduate Students

Many librarians struggle with how to balance scalable instructional offerings with more personalized instruction for online students—scale tends to come at the cost of personalization and vice versa. This paper will discuss three methods for connecting with online students that range from one-to-one personalization to widely scalable instruction: individual consultations, scaffolded instructional webinars, and asynchronous video tutorials. The student engagement with each type of instruction will be discussed, with ideas for future directions provided.     

面向查询的观点摘要模型研究:以Debatepedia为数据源

本文系统性地研究面向查询的观点摘要任务,旨在构建一种查询式观点摘要模型框架,探究不同的摘要方法对摘要效果的影响。通过综合考虑情感倾向与句子相似度,从待检文档中抽取出待摘要语句,再结合神经网络和词嵌入技术生成摘要,进而构建面向查询的观点摘要框架。从Debatepedia网站上爬取议题和论述内容构建观点摘要实验数据集,将本文方法应用到该数据集上,以检验不同模型的效果。实验结果表明,在该数据集上,仅使用基于抽取式的方法生成的观点摘要质量更高,取得了最高的平均ROUGE分数、深度语义相似度分数和情感分数,较生成式方法分别提高6.58%、1.79%和11.52%,而比组合式方法提高了8.33%、2.80%和13.86%;同时,本文提出的句子深度语义相似度和情感分数评估指标有助于更好地评估面向查询的观点摘要模型效果。研究结果对于提升面向查询的观点摘要效果,促进观点摘要模型在情报学领域的应用具有重要意义。     

基于深度学习的期刊分群与科学知识结构测度方法研究

准确地研究和测度科学知识之间的逻辑关系和结构体系,是进行科学政策研究和科研项目资助布局等科研管理活动的重要基础。学术期刊作为科学知识传播和交流的重要平台,是探测科学知识结构的一种有效载体,但是不同的学术期刊分类体系对科学知识结构的测度结构会产生直接而广泛的影响。文章从学术期刊分群的角度出发,考虑期刊在共被引过程中的距离因素,通过采用深度学习算法,来进行期刊的相似度计算与分群问题研究,在此基础上进行科学知识结构测度方法研究,并以中国人文社会科学期刊引文数据库为实验对象进行了实证研究。从实证结果来看,我国人文社会科学学科知识结构存在较为明显的结构划分,不同学科类别或不同研究领域的期刊都被分到了相应的群组,表明从期刊使用的角度来看,我国人文社会科学知识结构边界是相对较为清晰的。在此基础上重点对法学期刊的两个群组的科学研究主题进行了挖掘,从关键词的共现网络中可以明显看出,两个期刊群体内的研究主题虽有一定的交叉,但是两者在具体研究内容上也存在着显著区别。     

情报学领域人工智能相关研究的文献计量分析及探析

[目的/意义]作为一门与信息技术密切关联的学科,近年来快速发展的人工智能为情报学带来了诸多影响。有效梳理和整合这些影响的发展脉络与应用场景,强化人工智能在当前情报工作中的角色与作用,能为情报学相关研究的发展提供一定的参考与启示。[研究设计/方法]聚焦情报学领域人工智能相关研究,从高被引论文、文献共引、主题词共现三个维度对人工智能在情报学中的知识基础及其演化和研究热点予以分析与解读。[结论/发现]归纳了人工智能研究对情报学的影响,主要包括促进情报学研究主题的衍生和情报学研究方法的革新两方面,并从理论创新、研究对象拓展、信息服务提升与学科建设四个方面探讨人工智能在情报学领域可能的应用。[创新/价值]通过对情报学领域人工智能相关研究进行深入量化分析和定性研究,探讨和揭示了该主题下研究热点、研究趋势和相关发展规律,为相关领域学者提供一定借鉴与启发。     

Tailoring an information skills programme for Trainee Nursing Associates

This feature describes the tailored information skills programme which was delivered for the second cohort of Trainee Nursing Associates (TNAs) at Barnsley Hospital NHS Foundation Trust and presents the results of the evaluation which was undertaken. Literature searching and critical appraisal were offered to the first cohort. Feedback was collated, and sessions were refined and tailored to better meet the information needs of the second cohort of TNAs. A comparison of confidence ratings before and after the programme ascertained whether the information skills programme had a positive benefit in confidence levels of attendees. D.I.     

基于机器学习的图书馆用户流失影响因素研讨

论文针对图书馆用户流失问题,基于图书馆用户数据,构建用户特征矩阵,利用深度神经网络预测用户流失风险;将存在流失风险的用户作为重点研究对象,根据用户特征,结合S-O-R理论设计图书馆用户流失影响因素模型。采用扎根理论方法设计问卷,提取影响图书馆用户流失的指标因素,并构建结构方程模型进行分析,将技术研究与质性研究相结合,挖掘影响用户流失的核心因素,并在此基础上,构建了图书馆智慧型服务空间设计模型,为图书馆创新服务提供决策支持。     

Identification of highly-cited papers using topic-model-based and bibliometric features: the consideration of keyword popularity

The number of received citations have been used as an indicator of the impact of academic publications. Developing tools to find papers that have the potential to become highly-cited has recently attracted increasing scientific attention. Topics of concern by scholars may change over time in accordance with research trends, resulting in changes in received citations. Author-defined keywords, title and abstract provide valuable information about a research article. This study performs a latent Dirichlet allocation technique to extract topics and keywords from articles; five keyword popularity (KP) features are defined as indicators of emerging trends of articles. Binary classification models are utilized to predict papers that were highly-cited or less highly-cited by a number of supervised learning techniques. We empirically compare KP features of articles with other commonly used journal-related and author-related features proposed in previous studies. The results show that, with KP features, the prediction models are more effective than those with journal and/or author features, especially in the management information system discipline.     

Application of machine learning techniques to assess the trends and alignment of the funded research output

Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada.     

我国馆校结合领域研究热点及发展趋势——基于共词分析法的可视化研究

深度认识国内馆校结合领域研究的主题和发展趋势,不仅有利于研究者更加详细、清楚地了解国内馆校结合发展的最新动态,也有利于推动我国馆校结合的发展。研究结果表明,我国馆校结合领域研究主要集中在馆校结合中的博物馆角色研究和馆校结合的结合特征研究两大方面,其中馆校结合的结合特征研究又可分成六个方面:馆校结合教育课程建设;馆校结合课程的教育学思考;馆校结合教育模式;馆校结合相关教育类型;馆校结合机制与策略;馆校结合现状。我国馆校结合领域研究的发展趋势为:奠定博物馆在馆校结合中的重要作用;馆校结合机制的研究日趋增加;更多体现馆校结合课程中的教育学理念;完善馆校结合评价体系。     

Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.