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排序方式: 共有285条查询结果,搜索用时 15 毫秒
1.
专业搜索引擎在高校图书馆个性化信息服务中的应用 总被引:1,自引:0,他引:1
阐述了专业搜索引擎及其技术特点,针对高校图书馆个性化信息服务中科研和专业学科的特殊需要,运用专业搜索引擎的技术优势和特点,为用户提供专业学科信息资源的搜集,进一步增强高校图书馆个性化信息系统的服务能力.参考文献4. 相似文献
2.
试论数字图书馆个性化服务中的个人信息保护 总被引:2,自引:0,他引:2
用户个人信息是数字图书馆开展个性化服务的基础和核心,加强个人信息保护,对维护用户的合法权益,促进数字图书馆社会功能的充分发挥具有重大现实意义。数字图书馆在开展个性化服务过程中,应遵循知情同意、妥当合适、限制利用、用户参与、安全保障等原则,运用法律、法规进行约束,实行规范的自律措施,并辅以用户的自我保护, 以切实做好对个人信息的保护。 相似文献
3.
图书馆个性化信息服务研究述评 总被引:14,自引:0,他引:14
本文利用《中国科技期刊数据库》和《中国学术期刊网》等检索工具,对我国有关图书馆个性化信息服务的研究论文进行了定量和定性方面的分析,并提出了今后研究方向。 相似文献
4.
个性化推荐系统中的用户模型问题 总被引:2,自引:0,他引:2
个性化推荐系统已成为图书馆提供个性化服务的重要手段,而用户模型则是个性化推荐系统的基础和核心。然而基于关键词的用户模型已经不能够适应用户的个性化信息需求,本文提出了基于ontology的用户模型的设想。 相似文献
5.
论网站信息咨询服务模式 总被引:15,自引:0,他引:15
目前,对于网站信息咨询概念的认识尚不统一。网站信息咨询的主要模式有:基于资源重组的知识化信息服务,依托应用系统的智能化服务,基于用户需求的个性化服务,基于实时联动的交互式咨询服务等等。网站咨询服务具有快捷方便、超越时空、动态发布、资源共享、智能管理、安全可靠、实时交互、服务主动等优点。其存在的问题主要表现于思想认识、经费投入和技术、法规保证方面。参考文献8。 相似文献
6.
基于军事训练网的个性化信息服务研究 总被引:4,自引:0,他引:4
个性化信息服务是指能够满足用户个人信息需求的一种信息服务。文章在分析基层通信连队用户群体的信息需求特点基础上,提出在军事训练网上构建面向基层连队的个性化信息系统,并结合实际介绍了该系统的设计和实现方法。 相似文献
7.
We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation. 相似文献
8.
Julie M. Kallio Richard Halverson 《Journal of Research on Technology in Education》2020,52(3):371-390
AbstractPersonalized learning refers to a collection of practices designed to place student interests and needs at the heart of schooling. Schools that implement personalized learning need leaders that support educators and students in redesigning the core practices of teaching and learning in K-12 schools. To answer the question of how leaders support this redesign, we use distributed leadership theory to focus on the macrotasks and microtasks that leaders enact to create the conditions for personalized learning practices. Drawing on a five-year, qualitative study of 11 personalized learning programs in the Midwest, we identify three macrotasks supporting personalized learning: reorganizing learning environments to support student voice and choice, assembling idiosyncratic technology ecosystems to distribute teaching and learning tasks, and redesigning instructional time to prioritize student’s interests, agency, and learning relationships. After we describe a number of microtasks associated with each macrotask, we discuss how a consideration of these kinds of leadership tasks can open the contemporary discussion of personalized learning from a narrow focus on learning technologies to an expansive vision of student-centered school reform. 相似文献
9.
Kathryn S. McCarthy Micah Watanabe Jianmin Dai Danielle S. McNamara 《Journal of Research on Technology in Education》2020,52(3):301-321
AbstractComputer-based learning environments (CBLEs) provide unprecedented opportunities for personalized learning at scale. One such system, iSTART (Interactive Strategy Training for Active Reading and Thinking) is an adaptive, game-based tutoring system for reading comprehension. This paper describes how efforts to increase personalized learning have improved the system. It also provides results of a recent implementation of an adaptive logic that increases or decreases text difficulty based on students’ performance rather than presenting texts randomly. High school students who received adaptive text selection showed increased sense of learning. Adaptive text selection also resulted in greater pre-training to post-training comprehension test gains, especially for less-skilled readers. The findings demonstrate that system-driven, just-in-time support consistent with the goals of personalized learning benefit the efficacy of computer-based learning environments. 相似文献
10.
《International Journal of Information Management》2016,36(5):784-792
The advancement in mobile technology and the introduction of cloud computing systems enable the use of educational materials on mobile devices for a location- and time-agnostic learning process. These educational materials are delivered in the form of data and compute-intensive multimedia-enabled learning objects. Given these constraints, the desired objective of mobile learning (m-learning) may not be achieved. Accordingly, a number of m-learning systems are being developed by the industry and academia to transform society into a pervasive educational institute. However, no guideline on the technical issues concerning the m-learning environment is available. In this study, we present a taxonomy of such technical issues that can impede the life cycle of multimedia-enabled m-learning applications. The taxonomy is devised based on the issues related to mobile device heterogeneity, network performance, content heterogeneity, content delivery, and user expectation. These issues are discussed, along with their causes and measures, to achieve solutions. Furthermore, we identify several trending areas through which the adaptability and acceptability of multimedia-enabled m-learning platforms can be increased. Finally, we discuss open challenges, such as low complexity encoding, data dependency, measurement and modeling, interoperability, and security as future research directions. 相似文献