Making sense of learner and learning Big Data: reviewing five years of Data Wrangling at the Open University UK

Most distance learning institutions collect vast amounts of learning data. Making sense of this ‘Big Data’ can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated in 2012 a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical and sense-making advice to staff and senior management. Given substantial changes within the OU (e.g. new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case study provides an in-depth review of lessons learned of five years of data wrangling. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.     

Objectivity as standardization in data-scientific education policy,technology and governance

ABSTRACT

New data-driven technologies appear to promise a new era of accuracy and objectivity in scientifically-informed educational policy and governance. The data-scientific objectivity sought by education policy, however, is the result of practices of standardization and quantification deployed to settle controversies about the definition and measurement of human qualities by rendering them as categories and numbers. Focusing on the emerging policy agenda of ‘social and emotional learning and skills,’ this paper examines the practices of ‘objectivity-making’ underpinning this new field. Objectivity-making depends on three translations of (1) scientific expertise into standardized and enumerable definitions, (2) standardization into measurement technologies, and (3) the data produced through measurement technologies into objective policy-relevant knowledge, which consolidates a market in SEL technologies. The paper sheds light on knowledge-making practices in the era of big data and policy science, and their enduring reliance on the precarious construction of objectivity as a key legitimator of policy-relevant scientific knowledge and ‘evidence-based’ education governance.     

基于视频的数据挖掘系统的研究

研究了一种视频数据挖掘的系统结构.介绍了该系统中各主要模块的功能;描述了视频数据预处理和视频数据立方体;讨论了视频数据挖掘方法并探讨了典型的视频挖掘技术.     

Metaphors we’re colonised by? The case of data-driven educational technologies in Brazil

ABSTRACT

This article discusses issues concerning the spread of data-driven educational technologies in Brazil. Here, as elsewhere, educational technology continues to be promoted optimistically as the bearer of a panacea for historically-rooted social problems. Whilst some of these technologies have indeed contributed to important widening-participation programmes in the last two decades, widespread advocacy of technological ‘solutionism’, reflected in gradually stronger policy demands for efficiencies to be improved through ‘innovation’, has supported a relentless marketisation of the country’s educational systems. As transnational corporations position themselves to take control of key areas of these systems, threatening to restructure the whole sector, data-driven educational technologies provide the latest example in a series of ‘new’ ideas offered in an ever-expanding market. Based on the notion of ‘conceptual metaphors’, which encapsulate specific ways of perceiving, thinking and relating with the world, this article examines key metaphors underpinning discourses surrounding data-driven educational technologies in Brazil. In particular, the article analyses ways in which these specific metaphors may be promoting perspectives that ignore difference and obscure broader questions concerning education, thus contributing to the reproduction of previously existing problems and supporting new forms of colonisation.     

企业新计量信息流转模型和新自动计量模型设计与实施的应用研究

文章讨论了基于信息化数据通讯流转原理和现场自动控制理论,研究了冶金制造企业大宗原燃料、成品、半成品和产品新计量信息流转模型和新自动计量模型设计与实施的现代信息流转和自动称量达到自动、稳定、正确、可靠、合理、可追溯的问题,通过在本钢集团公司ERP、EMS系统配套运行和现场轧钢线计量称重实施等线上操作的手段来实现应用研究。解决了企业新计量信息流转模型和新自动计量模型设计与实施的问题,达到了计量信息系统的提出"数出一门,量出一家"和"网络化自动运行"要求,为冶金制造行业解决此类问题提供了新的思路和方法,具有极大的推广和应用价值。     

内部会计控制的若干问题

内部会计控制制度是企事业单位加强内部会计监督的一项基础管理措施，目前在部分企事业单位中由于制度建设不健全，内部控制管理松懈，会计监督失控，出现了一些经济犯罪和腐败现象，为此，笔者就内部会计控制的若干问题进行探讨，提出相应的对策。     

基于数据仓库的CRM应用研究

客户关系管理（CRM）逐渐成为企业的焦点，也是数据仓库技术的重要应用领域，本文介绍了客户信息管理以及数据仓库技术，分析了数据仓库技术在CRM中的应用；阐述了目前数据仓库应用面临的困境；并从应用及技术的层面，分析了成功实现基于数据仓库的CRM的关键点．     

Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach

This paper applies the production framework associated with the data envelopment analysis (DEA) method to evaluate the relative efficiency of R&D activities across countries. R&D capital stocks and manpower are treated as inputs while patents and academic publications are considered as outputs. A three-stage approach, which involves using DEA for evaluating efficiency and using Tobit regressions for controlling the external environment, is applied to 30 countries in recent years. The results show that less than one-half of the countries are fully efficient in R&D activities and that more than two-thirds are at the stage of increasing returns to scale. Most countries have a more significant advantage in producing SCI cum EI publications than in generating patents.     

APRIRIO算法的扩展算法的提出

进行数据处理的时候，总希望能在繁杂的海量数据中挖掘出有用的信息，因此就有了各种数据挖掘方法的产生，本文介绍一种APRIRIO算法的扩展算法，这种方法能够在众多数据中挖掘出反面规则来，从而得到更清晰的数据概念，方便得出更有用的信息。     

基于B/S模式的海量数据报表的实现

本文基于海量数据和网络打印速度之间的矛盾问题,通过几种报表实施方案的对比,提出了利用操纵Excel引擎方式解决网络打印海量数据报表问题的方法。以某煤矿企业考勤综合管理系统的考勤报表为应用背景,研究了基于B/S模式的海量数据报表的实现过程,讨论了客户端与服务器端的设置方式。应用证明该方法切实可行,可以有效地解决网络打印海量数据报表效率低的问题。