后数字时代的教育研究

目前,教育研究面临着大数据和算法的挑战。通过数据收集和操作的自动化处理,大数据和算法带来新的研究机会。然而,这些机会受到各种数据和算法偏见的严重制约。大数据和算法引入新的机遇和限制,很大程度上改变了当代教育研究。这些转变与教育政治、教育政策和教育实践等辩证地交织在一起。我们从事研究和应用研究的方式之间存在错综复杂的相互作用,这带来一系列哲学、方法论和实践上的全新问题和挑战。在混乱且变化莫测的后数字状况下,研究、政治、政策和实践处于永恒的互动之中,不能脱离彼此。在后数字时代,教育研究人员应抓住机会,以负责任和可持续的方式引领这些变革。     

Governing software: networks,databases and algorithmic power in the digital governance of public education

This article examines the emergence of ‘digital governance’ in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software that are being discursively promoted in education by cross-sectoral intermediary organizations. Such intermediaries, including National Endowment for Science, Technology and the Arts, Demos, the Innovation Unit, the Education Foundation and the Nominet Trust, are increasingly seeking to participate in new digitally mediated forms of educational governance. Through their promotion of network-based pedagogies and database-driven analytics software, these organizations are seeking to delegate educational decision-making to socio-algorithmic forms of power that have the capacity to predict, govern and activate learners' capacities and subjectivities.     

Assessment,technology and democratic education in the age of data

This paper contends that powerful techniques to manipulate data, enabled by technological and economic developments, can be easily co-opted to serve the restrictive frameworks of hyper-controlling, managerial accountability that characterise current cultures of summative assessment in education. In response to these challenges, research is urgently needed to increase our understanding of the impact that assessments have on individuals and society. The paper concludes that social research ought to contribute to the identification of responses – educational, technological and political – that can minimise inequalities and potential abuses through the encouragement of data literacy across society.     

A systematic review on the deployment and effectiveness of data analytics in higher education to improve student outcomes

Abstract

This is a systematic review conducted of primary research literature published between 2007 and 2018 on the deployment and effectiveness of data analytics in higher education to improve student outcomes. We took a methodological approach to searching databases; appraising and synthesising results against predefined criteria. We reviewed research on the effectiveness of three differentiated forms of data analytics: learning, academic and learner analytics. Student outcomes are defined as retention, academic performance and engagement. Our results find that three quarters of studies report the use of educational data analytics to be effective in improving student outcomes but their relationship with student outcomes requires further and more robust investigation and assessment. We argue that research must interpret and communicate effectiveness qualitatively, as well as quantitatively, by including the student voice in assessments of impact.     

Reconceptualising critical digital literacy

While it has proved a useful concept during the past 20 years, the notion of ‘critical digital literacy’ requires rethinking in light of the fast-changing nature of young people's digital practices. This paper contrasts long-established notions of ‘critical digital literacy’ (based primarily around the critical consumption of digital forms) with the recent turn towards ‘digital design literacy’ (based around the production of digital forms). In doing so, three challenges emerge for the continued relevance of critical digital literacy: (1) the challenge of critiquing the ideological concerns with the digital without alienating the individual's personal affective response; (2) connecting collective concerns to do with social and educational inequalities to individual practices; and (3) cultivating a critical disposition in a context in which technical proficiency is prioritised. The paper then concludes by suggesting a model of ‘critical digital design’, offering a framework that might bridge the divide between critical literacy models and the more recent design-based literacy models.     

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。     

Innovation in teacher education: towards a critical re-examination

ABSTRACT

In the course of introducing a themed issue of the journal on “Innovation in Teacher Education’, we lay out an argument for re-examining the meaning of innovation in the field, shifting it away from the dominance of the economistic and technological. Acknowledging its status as a ‘buzzword’, we distinguish between purposes for innovation and, in particular, between changes driven by arguments for social mobility and those driven by social justice and equity. Two imperatives for innovation underpinned by arguments for social justice and equity are identified: the concept of a ‘teacher education debt’, built on Ladson-Billings’ more general notion of ‘education debt’; and the humanization of learning, teaching and becoming a teacher as person-centred, relational practices. The final section of the article introduces each of the six papers in the context of the discussion in previous sections about these imperatives for change.     

Towards a radical digital citizenship in digital education

In this article, we attempt to define and explore a concept of ‘radical digital citizenship’ and its implications for digital education. We argue that the ‘digital’ and its attendant technologies are constituted by on-going materialist struggles for equality and justice in the Global South and North which are erased in the dominant literature and debates in digital education. We assert the need for politically informed understandings of the digital, technology and citizenship and for a ‘radical digital citizenship’ in which critical social relations with technology are made visible and emancipatory technological practices for social justice are developed.     

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.

     

高校数字图书馆在创新教育中的地位与作用

高校数字图书馆在创新教育中有着重要的地位和作用,扮演着重要的角色。它是高校实施创新教育的有力保障。作为高校数字图书馆应在创新教育中充分发挥起住信息服务的功能。     

高等教育评价的数字依附及消解

破“五唯”是引导高等教育评价走向科学与理性的制度性、理论性共识,其实质是消解高等教育评价的数字依附。高等教育评价的数字依附主要表现在数字广泛渗透到高等教育评价的各个角落;数字成为高等教育评价坐标系的优先尺度;非理性数字异化为高等教育评价的规训力量。究其本源,高等教育评价的数字依附源于技术理性支配下的理性主义评价观长期制约;高等教育场域简单化认知的推波助澜;数字得以彰显的资源黑洞与利益诱导。为此,还原数字在高等教育评价中的本真意义、明确高等教育评价中数字运用的合理边界、结构性优化高等教育评价体系当是消解数字依附、践行破“五唯”的关键。     

AI in education: learner choice and fundamental rights

ABSTRACT

This article examines benefits and risks of Artificial Intelligence (AI) in education in relation to fundamental human rights. The article is based on an EU scoping study [Berendt, B., A. Littlejohn, P. Kern, P. Mitros, X. Shacklock, and M. Blakemore. 2017. Big Data for Monitoring Educational Systems. Luxembourg: Publications Office of the European Union. https://publications.europa.eu/en/publication-detail/-/publication/94cb5fc8-473e-11e7-aea8-01aa75ed71a1/]. The study takes into account the potential for AI and ‘Big Data’ to provide more effective monitoring of the education system in real-time, but also considers the implications for fundamental human rights and freedoms of both teachers and learners. The analysis highlights a need to balance the benefits and risks as AI tools are developed, marketed and deployed. We conclude with a call to embed consideration of the benefits and risks of AI in education as technology tools into the development, marketing and deployment of these tools. There are questions around who – which body or organisation – should take responsibility for regulating AI in education, particularly since AI impacts not only data protection and privacy, but on fundamental rights in general. Given AI’s global impact, it should be regulated at a trans-national level, with a global organisation such as the UN taking on this role.     

Mapping the digital divide in Britain: implications for learning and education

The internet presents many potential opportunities for people to learn for both formal and informal purposes. However, not everyone is able to make the most of the internet for learning. This paper utilises quantitative nationally representative survey data of internet use in Britain in order to explore the digital divide in relation to learning activities online. The results from this analysis give a detailed picture of the digital divide in Britain; illustrating those who are non‐users and users of the internet and the reasons that are important in explaining the diversity in non‐use and use of the internet for learning (e.g., age, educational background, skills, attitudes and experience). The findings may assist in the development of policies that seek to support under‐served groups to make the most effective use of the internet for formal and informal learning opportunities.     

职业教育数字化资源区域服务平台架构

应用先进的信息技术构建职业教育数字化资源区域服务平台顺应快速发展职业教育的时代需求,有利于促进优质教学资源的交流和共享。以促进学校资源、企业实践资源和社会资源有效融合为创建理念,从功能层和存储层两个层次实施区域服务平台构建,制定职业教育数字化资源区域服务流程,对职业教育信息化发展具有实践应用价值。     

基于远程教育系统的数字化社区学院建设研究

在介绍国外社区学院和远程教育成功经验的基础上,分析了我国社区学院和远程教育的发展现状.提出我国的数字化社区学院应具有系统性、开放性、灵活性等特点.构建我国的数字化社区学院,政府要大力支持社区学院建设,以现有的全国广播电视大学为基本管理系统,以数字化学习港为平台,加强优质数字化资源建设.     

"有教无类"辨析

“有教无类”是孔子的教化主张，而未必是他从教实践的表述。以孔子的其他言论和孟子、荀子的见解作为参证，对“教”与“诲”进行辨析，藉以说明“以今度古”的不当。     

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.