Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.     

The Givenness of the Human Learning Experience and Its Incompatibility with Information Analytics

Abstract

The rise of learning analytics, the application of complex metrics developed to exploit the proliferation of ‘Big Data’ in educational work, raises important moral questions about the nature of what is measurable in education. Teachers, schools and nations are increasingly held to account based on metrics, exacerbating the tendency for fine-grained measurement of learning experiences. In this article, the origins of learning analytics ontology are explored, drawing upon core ideas in the philosophy of computing, such as the general definition of information and the information-theoretic account of knowledge. Drawing upon a reading of Descartes Meditatio II, which extends the phenomenology of Jean-Luc Marion into a pedagogy of intentionality, the article identifies a fundamental incompatibility between the subjective experience of learning and the information-theoretic account of knowledge. Human subjects experience and value their own information incommensurably with the ways in which computers measure and quantify information. The consequences of this finding for the design of online learning environments, and the necessary limitations of learning analytics and measurement are explored.     

CLAIRE: A combinatorial visual analytics system for information retrieval evaluation

Information Retrieval (IR) develops complex systems, constituted of several components, which aim at returning and optimally ranking the most relevant documents in response to user queries. In this context, experimental evaluation plays a central role, since it allows for measuring IR systems effectiveness, increasing the understanding of their functioning, and better directing the efforts for improving them. Current evaluation methodologies are limited by two major factors: (i) IR systems are evaluated as “black boxes”, since it is not possible to decompose the contributions of the different components, e.g., stop lists, stemmers, and IR models; (ii) given that it is not possible to predict the effectiveness of an IR system, both academia and industry need to explore huge numbers of systems, originated by large combinatorial compositions of their components, to understand how they perform and how these components interact together.We propose a Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE) which allows for exploring and making sense of the performances of a large amount of IR systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together.The CLAIRE system is then validated against use cases based on several test collections using a wide set of systems, generated by a combinatorial composition of several off-the-shelf components, representing the most common denominator almost always present in English IR systems. In particular, we validate the findings enabled by CLAIRE with respect to consolidated deep statistical analyses and we show that the CLAIRE system allows the generation of new insights, which were not detectable with traditional approaches.     

A survey towards an integration of big data analytics to big insights for value-creation

Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.     

A longitudinal study of the actual value of big data and analytics: The role of industry environment

Despite the popularity of big data and analytics (BDA) in industry, research regarding the economic value of BDA is still at an early stage. Little attention has been paid to quantifying the longitudinal impact of organizational BDA implementation on firm performance. Grounded in organizational learning theory, this study empirically demonstrates the impact of BDA implementation on organizational performance and how industry environment characteristics moderate the BDA-performance relationships. Using secondary data regarding BDA implementation from 2010 to February 2020, we find that BDA implementation has a significant impact on two types of business value creation: operational efficiency and business growth. Furthermore, the impact of BDA on operational efficiency is amplified in less dynamic and complex environments, while the BDA-business growth relationship is more pronounced in more dynamic, complex, and munificent environments. Collectively, this study provides a theory-centric understanding of BDA’s economic benefits. The findings offer insights to firms about what actual benefits BDA implementation may generate and how firms may align the use of BDA with the industry environments they are operating in.     

基于关联挖掘的在线学习行为规律实证分析研究

当前,在线教育已经成为现代教育的重要组成部分,为教育形式的创新开拓了无限的可能。但是,以慕课为代表的在线教育的课程注册人数通常远远高于最终完成课程的人数,课程完成率较低。该文探索影响在线学习效果特别是课程完成率的因素,并据此提出改进建议。具体采取了实证研究的方法,运用了关联规则挖掘技术和WEKA数据挖掘开源工具,对学堂在线平台上39门课程的学习记录数据进行分析,得出了一系列基于大数据的、有指导意义的在线学习行为方面的关联规则,为进一步开展后续研究提供了参考。     

大数据时代学习分析与高职院校教育改革

面对来势汹涌的大数据时代,如何有效度量、发现、评估与操纵信息化学习管理系统中积累的海量数据,已成为未来高职院校教育改革过程中不可回避的棘手问题。学习分析是大数据时代高职院校教育改革的助推器,是解决这一现实问题的有效途径之一。从高职院校教育改革在大数据时代所遭遇的挑战出发,阐述学习分析技术对高职院校教育改革的推动作用,并从高职院校教育管理、教师教学与学生学习三个方面分析学习分析技术对高职院校教育改革的影响。     

Big data analytics adoption: Determinants and performances among small to medium-sized enterprises

Big data analytics (BDA) adoption is a game-changer in the current industrial environment for precision decision-making and optimal performance. Nonetheless, the determinants or consequences of its adoption in small and medium enterprises remain unclear, hence the objective of this study. Data analysis of 171 Iranian small and medium manufacturing firms revealed that complexity, uncertainty and insecurity, trialability, observability, top management support, organizational readiness, and external support affect significantly on BDA adoption. The findings confirm the strong impact of BDA adoption in small to medium-sized enterprises, marketing and financial, performance enhancement. Understanding the drivers of BDA adoption helps managers to employ appropriate initiatives that are vital for effective implementation. The results enable BDA service providers to attract and diffuse BDA in small to medium-sized enterprises.     

政务热线大数据赋能城市治理创新:价值、现状与问题

[目的/意义]政务热线是政务服务与公众参与的重要渠道,对其积累的海量数据进行分析,可有效探知公众与企业的需求并解决城市治理问题。对政务热线数据的管理和应用水平进行评估,有利于其在城市治理创新中发挥应有的价值。[研究设计/方法]基于2020年政务热线发展研究报告的问卷调查数据和部分城市政务热线部门的案例分析和深度访谈,对政务热线大数据的利用现状与存在问题进行梳理和分析。[结论/发现]政务热线的数据治理状况总体较好,但是在大数据分析方面与城市治理需求还有较大差距。大数据的归集、分析、应用和管理都还处于发展初步,需要制定战略、增强能力、加强应用和推动跨部门协同。[创新/价值]从战略管理、能力建设、数据共享与开放等方面提出政策建议,以期推动政务热线大数据在城市治理创新中发挥作用。     

How well centrality measures capture student achievement in computer-supported collaborative learning? – A systematic review and meta-analysis

Research has shown the value of social collaboration and the benefits it brings to learners. In this study, we investigate the worth of Social Network Analysis (SNA) in translating students' interactions in computer-supported collaborative learning (CSCL) into proxy indicators of achievement. Previous research has tested the correlation between SNA centrality measures and achievement. Some results indicate a positive association, while others do not. To synthesize research efforts, investigate which measures are of value, and how strong of an association exists, this article presents a systematic review and meta-analysis of 19 studies that included 33 cohorts and 16 centrality measures. Achievement was operationalized in most of the reviewed studies as final course or task grade. All studies reported that one or more centrality measures had a positive and significant correlation with, or a potential for predicting, achievement. Every centrality measure in the reviewed sample has shown a positive correlation with achievement in at least one study. In all the reviewed studies, degree centralities correlated with achievement in terms of final course grades or other achievement measure with the highest magnitude. Eigenvector-based centralities (Eigenvector, PageRank, hub, and authority values) were also positively correlated and mostly statistically significant in all the reviewed studies. These findings emphasize the robustness and reliability of degree- and eigenvector-based centrality measures in understanding students’ interactions in relation to achievement. In contrast, betweenness and closeness centralities have shown mixed or weak correlations with achievement. Taken together, our findings support the use of centrality measures as valid proxy indicators of academic achievement and their utility for monitoring interactions in collaborative learning settings.