Data entry: towards the critical study of digital data and education

The generation and processing of data through digital technologies is an integral element of contemporary society, as reflected in recent debates over online data privacy, ‘Big Data’ and the rise of data mining and analytics in business, science and government. This paper outlines the significance of digital data within education, arguing for increased interest in the topic from educational researchers. Building on themes from the emerging sub-field of ‘digital sociology’, the paper outlines a number of ways in which digital data in education could be questioned along social lines. These include issues of data inequalities, the role of data in managerialist modes of organisation and control, the rise of so-called ‘dataveillance' and the reductionist nature of data-based representation. The paper concludes with a set of suggestions for future research and discussion, thus outlining the beginnings of a framework for the future critical study of digital data and education.     

Contextualizing privacy with wearable data in higher education

This conceptual study uses dynamic systems theory (DST) and phenomenology as lenses to examine data privacy implications surrounding wearable devices that incorporate stakeholder, contextual and technical factors. Wearable devices can impact people's behaviour and sense of self, and DST and phenomenology provide complementary approaches for emphasizing the subjective experiences of individuals that occur with the use of wearable data. Privacy is approached through phenomenology as an individual's lived bodily experience and DST emphasizes the self-regulation and feedback loops of individuals and their uses of wearable data. The data collection, analysis and communication of wearable data to support learning systems alongside privacy implications for each are examined. The IoT, cloud computing, metadata and algorithms are discussed as they relate to wearable data, pointing out privacy risks and strategies to minimize harm.

Practitioner notes

What is already known about this topic

• Data privacy is a complex topic and is approached through different perspectives, influencing the degree of an individual's data autonomy.
• Wearable technology is increasing in the consumer market and offers great potential to learning environments.

What this paper adds

• Extends extant literature on dynamic systems theory and phenomenology, contributing these perspectives to educational research in the context of student data privacy and wearable technologies.
• Provides a framework to understand the complex and contingent ways that privacy can be understood in the collection, analysis, and communication of wearable data to support learning.

Implications for practice and/or policy

• Higher education faculty and educational policymakers should consider various interactions in systems and among systems of how wearable data collection may be analysed, communicated and stored, potentially exposing students to privacy harms.
• Multiple actors in learning systems must engage in continuous and evolving feedback loops around data security, consent, ownership and control to determine who has access to student data, how it is used and for what purposes.
• The EU's General Data Protection and Regulation offers one of the most comprehensive frameworks for higher education institutions and faculty around the world to follow for protecting student data privacy.

     

网络数据安全与备份系统方案

对数据安全中的RAID技术以及数据备份的三种系统方案进行了分析,并在此基础上探讨了当前常用的数据备份设备。     

结构化数据及非结构化数据的分类方法

阐述了结构化数据和非结构化数据的基本概念以及分类过程;同时对目前应用于结构化数据和非结构化数据的主要的分类算法及其优缺点进行分析和比较.     

产品数据管理中的产品数据模型

产品数据模型是产品数据管理中信息集成的基础，本提出了基于产品对象的数据管理方式，在此基础上描述了PDM中的面向对象的产品数据模型。根据产品数据模型提出了基于产品对象的通用数据、设计数据、制造数据3棵树组织产品数据的方法，为现在并行产品设计方式提供了有力的支持。     

How do different stakeholders utilise the same data? The case of school leavers’ and graduates’ information systems in three European countries

This paper analyses how three European countries produce and use data within a specific educational policy field, that of school leaving and graduation. It compares how stakeholders in England, Finland and the Netherlands know what happens to the leavers from schools and universities. Through gathering evidence about the methodological underpinnings of the school leavers’ and graduates’ information systems (SLGIS) and whose data needs they aim to satisfy, this research provides insight into the discrepancies of data production. Moreover, as stakeholders from the policy and the institutional level are represented to a different extent when SLGIS are set up, the utility of the resulting data in their work is of differing degrees. The paper models the problems of how SLGIS are set up and utilised, thus suggesting some of the possible solutions. Using this particular example, the paper highlights the discrepancies of data production and utilisation in terms of the focus, the timing, the methodology and sampling as well as the processes of dissemination. To provide a wider relevance, the paper outlines key issues around the utility of research evidence.     

数据挖掘技术

数据挖掘是目前一种新的重要的研究领域。本文介绍了数据挖掘的概念、目的、常用方法、数据挖掘过程、数据挖掘软件的评价方法,对数据挖掘领域面临的问题作了分析和展望。     

Social inequalities in attaining higher education in Scotland: New evidence from sibling data

Over the last decades, various policies at national and local levels have been implemented to widen participation in higher education (HE) in Scotland and more widely in the UK. Despite this, the acquisition of a HE qualification is still largely determined by the family in which individuals are born. Our study provides new evidence on the extent to which family factors matter by examining sibling data from the Scottish Longitudinal Study, a large-scale linkage study created using data from administrative and statistical sources. Random effects linear probability models are used to analyse individual and family-level variance in the chances of obtaining a HE qualification. Our results show that about 40% of the variation in the chances of attaining a university degree is explained by siblings’ shared family characteristics and about a third of this share is explained by parental social class, education and housing tenure. A high degree of sibling similarity in the outcome was found across all social-origin classes. However, while siblings of advantaged families are alike because they both graduated from HE, siblings of disadvantaged families are alike because neither of them did. We suggest that parental compensatory strategies in the former families and economic constraints in the latter families may explain such stark patterns of inequality. Finally, we do not find evidence that the availability of sub-degrees makes a difference to these patterns.     

Distributed anonymous data perturbation method for privacy-preserving data mining

Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data perturbation techniques are comparatively efficient but are mainly used in centralized privacy-preserving data mining （PPDM）. In this paper, we propose a light-weight anonymous data perturbation method for efficient privacy preserving in distributed data mining. We first define the privacy constraints for data perturbation based PPDM in a semi-honest distributed environment. Two protocols are proposed to address these constraints and protect data statistics and the randomization process against collusion attacks： the adaptive privacy-preserving summary protocol and the anonymous exchange protocol. Finally, a distributed data perturbation framework based on these protocols is proposed to realize distributed PPDM. Experiment results show that our approach achieves a high security level and is very efficient in a large scale distributed environment.     

Analytics-supported reflective assessment for 6th graders' knowledge building and data science practices: An exploratory study

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.

Practitioner notes

What is already known about this topic

• Data use becomes increasingly important in the K-12 educational context.
• Little is known about how to scaffold students to develop data science practices.
• Knowledge Building (KB) and learning analytics-supported reflective assessment (AsRA) show premises in developing these practices.

What this paper adds

• AsRA-enhanced KB can help students improve KB and data science practices over time.
• AsRA-enhanced KB design benefits students of different-performing groups.
• AsRA-enhanced KB is accessible to elementary school students in science education.

Implications for practice and/or policy

• Developing a collaborative and reflective culture helps students engage in collaborative inquiry.
• Pedagogical approaches and analytic tools can be developed to support students' data-driven decision-making in inquiry learning.

     

Students' articulations of uncertainty about big data in an integrated modeling approach learning environment

In recent years, big data has become ubiquitous in our day-to-day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data—the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP.     

Measuring value-added in higher education: Possibilities and limitations in the use of administrative data

This paper develops a general methodology for measuring the value added of institutions of higher education using commonly available administrative data. Our approach recognizes the data limitations and selection problems inherent in higher education, and highlights the challenges these issues pose for education policy. Combining information from different administrative sources in the state of Texas, we follow the universe of Texas college applicants from the time of application (pre-enrollment) through public college and into the labor market. In specifications that do not control for selection, we find large, significant differences across colleges in terms of persistence, graduation, and earnings; however, these differences decrease substantially when we control for selection. In light of the growing interest in using value-added measures in higher education for both funding and incentivizing purposes, our methodology offers unique evidence and lessons for policy makers.     

应用Excel处理测量数据

应用Excel的计算法和图表法对各种技术测量中的数据进行处理,可以快速准确地实现测量数据的程序化和规范化处理,并生成所需图表。     

应用Excel处理测量数据

应用Excel的计算法和图表法对各种技术测量中的数据进行处理，可以快速准确地实现测量数据的程序化和规范化处理．并生成所需图表。     

The moving image in education research: Reassembling the body in classroom video data

While audio recordings and observation might have dominated past decades of classroom research, video data is now the dominant form of data in the field. Ubiquitous videography is standard practice today in archiving the body of both the teacher and the student, and vast amounts of classroom and experiment clips are stored in online archives. Yet little to no research interrogates how this data partakes in the history of scientific cinema, assembling particular images of teacher and student bodies, nor how the digital nature of video devices might indeed challenge theoretical assumptions about social science more generally. This paper situates current video research practices within the history of scientific cinema and opens up the discussion about how these practices are linked to shifting structures of industrial labor. The paper uses the work of Gilles Deleuze – on art, cinema and the body – as a means of provoking the reader to consider new ways of working with the moving image.     

网络中的数据加密技术

数据加密技术是实现网络安全的关键技术之一。该文系统地讨论了两种加密技术：对称式加密、公开密钥加密，并探讨了网络中的三种加密方式：链路加密、节点加密和端到端加密。     

基于Weka平台的不确定数据挖掘

阐述传统关联规则挖掘Apriori算法的基本原理和算法思想,针对只能处理位置已经被精确给定的对象,利用开源Weka平台,通过数据转换,处理不确定数据集,对鸢尾花数据进行测试实验,挖掘出一些有益的关联结果。     

云数据安全存储技术探讨

云数据计算因为其计算速度快、方便快捷、造价低廉等优势逐渐成为备受关注和欢迎的计算存储模式,但是随之而来的信息存储安全问题却困扰着众多的云数据用户,更使得众多的计算机用户望而却步。本文着重介绍了密文访问、加密存储、数据销毁三个方面对云数据安全存储技术进行了分析。