The role of big data analytics capabilities (BDAC) in understanding the challenges of service information and operations management in the sharing economy: Evidence of peer effects in libraries

Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms.     

大数据分析的方法及其在情报研究中的适用性初探

对基于数据或信息分析的情报研究来说,大数据分析方法带来了新机遇。文章在现有研究的基础上,先梳理了基于数据、流程及信息技术三种视角的大数据分析方法,并从中归纳出了面向统计、挖掘、发现、预测与集成等5种分析层次的17种相关研究方法。接着探讨了大数据分析方法在情报研究的适用性,分别找出10种可直接移植的方法、2种调整后可移植的方法、2种不适用的方法、3种需要继续研究或关注的方法。     

学习分析技术的发展和挑战——第四届学习分析与知识国际会议评析

第四届学习分析与知识国际会议于2014年3月24-28日在美国印第安纳州波利斯成功举行,会议以探讨学习分析研究、理论和实践的交叉点为主题,涵盖了学习分析技术在教育学、教育心理学、教育管理学、工程学中的运用,以及教育数据挖掘、计算机算法和数据可视化等方面的发展。文章首先说明了此次会议的背景,从研究、理论和实践三方面阐析学习分析主题之间的关系,简述了来自孟菲斯大学的格莱赛教授(Art Graesser)、香港大学的罗陆慧英(Nancy Law)教授和加州大学圣地亚哥分校的克莱默教授(Scott Klemmer)三位专家所作的主题报告;然后从学习分析与课程教学设计、教与学过程挖掘和评价、学习分析与学习资源、文本挖掘与语义分析、学习分析与数学教育、学习分析与教育一体化、学习分析多元化等七个方面对分论坛报告及会议进行系统综述;文章最后指出未来学习分析研究和发展的五个方向:逐步明晰学习分析系统概念与理论、研究通用性的算法和模型、研制学习分析技术标准、支撑数据驱动的学习和评估、融入教育信息化应用与实践、推进教育的深度发展和加快多元化进程,期望能够推动学习分析系统化研究和在教育中的深度应用。     

Proving Value and Improving Practice: Using System Data to Analyze User Behaviors

Today's library systems collect and supply a wealth of transaction data. The unobtrusive data collected by these systems can help us understand our users' search and information-retrieval behaviors. Analyzing user behaviors challenges or reinforces our practices, and the changes we make should not be simply consumer preferences but based upon the analysis of usage patterns and search behavior. Institutions of any size can use a cyclical grounded theory model to look at the unobtrusive data that is available from systems such as WorldCat Local, Google Analytics, and others to reveal our users' information-seeking behaviors.

The author's model shows how system-generated data can be a part of an assessment strategy for ongoing improvement that can be implemented by small academic libraries. By articulating inputs consisting of goals, users, and performance indicators, and by utilizing a grounded-theory approach, libraries can observe behaviors that can inform, as well as reveal, outputs.     

Tracking User Behavior with Google Analytics Events on an Academic Library Web Site

ABSTRACT

The primary purpose of an academic library Web site is to serve as a portal to library-acquired content. Navigational design of a library Web site affects the user's ability to find and access content. At Albertsons Library, the goal of the navigational design of the Web site is to mimic user behavior on the Web site to help them access information and articles from over 300 different library vendors. Coordinating with different vendors makes tracking the navigational flow of user behavior difficult with the tool Google Analytics. Using the events feature in Google Analytics, the team responsible for Web design was able to track user flow, and was able to quantify how many users were actual “drop-offs” versus those that were clicks into library resources. Decisions made after acquiring these data resulted in a Web site with a 10 percent or less bounce rate, and decreased the number of clicks required for users accessing the library's content.     

Data quality management,data usage experience and acquisition intention of big data analytics

Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.     

学习分析技术:挖掘大数据时代下教育数据的价值

当前,大数据时代已经来临,教育领域同样积累了海量数据。教育领域已经部署了众多的学习管理系统,在这些软件系统中存储着海量的学习者信息及学习过程数据。如何利用这些数据,使这些数据转变为信息、知识,并为教学决策、学习优化服务,已成为教育工作者以及学习者们所关注的内容。学习分析技术有助于发挥学习过程数据的价值,使数据成为审慎决策、过程优化的重要依据。该文介绍了国内外学习分析技术研究现状,归纳出学习分析技术的关键技术及分析模式,并以实例从不同用户视角包括管理者、辅导教师、学习者展示了学习分析技术在网络学习过程分析中的应用过程。     

How learners produce data from text in classifying clickbait

Text provides a compelling example of unstructured data that can be used to motivate and explore classification problems. Challenges arise regarding the representation of features of text and student linkage between text representations as character strings and identification of features that embed connections with underlying phenomena. In order to observe how students reason with text data in scenarios designed to elicit certain aspects of the domain, we employed a task-based interview method using a structured protocol with six pairs of undergraduate students. Our goal was to shed light on students' understanding of text as data using a motivating task to classify headlines as “clickbait” or “news.” Three types of features (function, content, and form) surfaced, the majority from the first scenario. Our analysis of the interviews indicates that this sequence of activities engaged the participants in thinking at both the human-perception level and the computer-extraction level and conceptualizing connections between them.     

人际沟通分析学在心理健康课程中的应用与效果检验

本文采用质性分析方法,对66份首都经贸大学人际沟通分析学作业进行分析,检验人际沟通分析学在大学生心理健康教育中的教学效果,结果发现：选修人际沟通分析学的学生在心理幸福感的六项机能上有不同程度的改善,意味着幸福感的提升。这一结果肯定了人际沟通分析学在心理健康教育中的作用。     

Temporal networks in collaborative learning: A case study

Social Network Analysis (SNA) has enabled researchers to understand and optimize the key dimensions of collaborative learning. A majority of SNA research has so far used static networks, ie, aggregated networks that compile interactions without considering when certain activities or relationships occurred. Compressing a temporal process by discarding time, however, may result in reductionist oversimplifications. In this study, we demonstrate the potentials of temporal networks in the analysis of online peer collaboration. In particular, we study: (1) social interactions by analysing learners' collaborative behaviour, part of a case study in which they worked on academic writing tasks, and (2) cognitive interactions through the analysis of students' self-regulated learning tactics. The study included 123 students and 2550 interactions. By using temporal networks, we show how to analyse the longitudinal evolution of a collaborative network visually and quantitatively. Correlation coefficients with grades, when calculated with time-respecting temporal measures of centrality, were more correlated with learning outcomes than traditional centrality measures. Using temporal networks to analyse the co-temporal and longitudinal development, reach, and diffusion patterns of students' learning tactics has provided novel insights into the complex dynamics of learning, not commonly offered through static networks.