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1、用数据学习：用可视化的数据来 支持教学、学习和评价的进行 Learning with Data: Visualization to Support Teaching, Learning, and Assessment 摘要 论文选择程 序 在这个领域 有贡献的人 （9） 用可视化的数据来支持教学评价、 学习评 价 （综述性文章） Within the established paradigm of learning analytics, this special issue has a specific focus upon factors of learning associated wit
2、h interactive data visualizations. 在建立学习分析范式这个特殊领域中，有一个具体的焦点，建 立在学习的有关因素与可视化的交互数据之间的关系。 Learning analytics use dynamic information about learners and learning environments, assessing, eliciting and analysing it, for real- time modelling, prediction, and optimization of learning processes, learning e
3、nvironments, and educational decision-making (Ifenthaler 2015). 学习分析是利用动态的信息来对学习者和学习者的学习环境， 进行评价、引出和分析，对学习过程、学习环境以及教育决策 的制定进行实时的建模、预测和优化。 摘要-第一段 Opportunities of learning analytics are fostering interactions between students and facilitators as well as the availability of personalised and adaptive h
4、elp and feedback from peer learners in near real-time (Ifenthaler and Widanapathirana 2014). 学习分析是培养学生和促进者（老师）之间进行交流的机 会，以及有益于个性化的和自适应，和得到来自于同伴学 习者实时的帮助和反馈。 摘要-第一段 The advent of big data requires new perspectives on data processing and analysis including advanced methods and tools to visualize data
5、for supporting learning processes. 大数据的到来，在数据处理和分析方面需要新的观点，也 包括先进的工具和方法对数据可视化的处理以支持学习的 过程。 The primary purpose of visualisations of big data is to communicate complex patterns nested in big data (Chen 2010). 对大数据可视化的主要目的是使复杂的模型和大数据相联 系。 摘要-第二段 However, not only the visualisation of numeric informati
6、on using dashboards are important areas of research. The visualisation of semantic content are an emerging field of research opening up new perspectives on natural language processing and in-depth analysis of text data. 然而，不仅仅将数字化的信息利用仪表盘转换为可视化是本研 究中很重要的领域。在自然语言处理和深度数据挖掘方面将语 义化内容的可视化也是非常火爆的研究领域。 Thi
7、s special issue of Technology, Knowledge and Learning features articles which showcase the latest advances in the field of data visualisation for learning. 关于这个特殊问题的相关技术、知识和学习结构的文章，展示 了在数据可视化领域的最新进展。 摘要-第二段 This special issue is assembled from an open call asking for full manuscripts by the end of M
8、ay 2015. Each submitted manuscript was assigned to at least two reviewers of the special issue review board. 这个特殊的问题是由一个开放性的要求组合的，要求要在 2015年5月底提交完整的初稿。每一次提交的手稿都会至少分 配给两位审稿人，并且这两个人都是这个特殊问题的董事会 成员。 1 Paper Selection Process 选择论文的过程 Based on the comments of the reviewers, authors were asked to submit t
9、heir revised manuscript by the end of December 2015 addressing the comments from the reviewers and from the editors. The final acceptance of manuscripts was completed by the end of January 2016. 在审稿人审稿意见的基础上，作者要在2015年9月底提交 他们的修改稿，并且已经将审稿人和编辑的问题都解决 了。最终定稿是在2016年1月底。 1 Paper Selection Process 选择论文的过程
10、This special section begins with a work-in-progress article, Exploratory analysis in learning analytics. The authors, David C. Gibson (Curtin University) and Sara de Freitas (Murdoch University), summarize the methods, observations, challenges and implications for exploratory analysis drawn from two
11、 learning analytics research projects. 这个特殊的部分开始于一篇工作的文章Exploratory analysis in learning analytics.这篇文章的作者David C. Gibson（澳大利亚科廷科技大学）和Sara de Freitas（ 莫道克大学），总结来自两个学习分析研究项目的探索性分 析的方法，观察，挑战和启示。 2 Contributors to this Special Section 对这个领域有贡献的人（1） Brett E. Shelton (Boise State University), Jui-Long Hu
12、ng (Boise State University) and Sarah Baughman (Boise State University) examine in Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention, a statistical analysis technique using both static and dynamic variables to determine which students are at-risk and when an int
13、ervention could be most helpful during a semester. Brett E. Shelton(美国博伊西州立大学)，Jui-Long Hung 和Sarah Baughman，测试在线研究生教师教育：建立学生 成功干预的心电图，采用动态和静态变量结合的统计分析 方法，为了确定出学生什么时候是在危险边缘和在一个学 期期间什么时候对学生采取干预最有帮助。 2 Contributors to this Special Section 对这个领域有贡献的人（2） What We Can Learn from the Data: A Multiple-Case
14、Study Examining Behavior Patterns by Students with Different Characteristics in Using a Serious Game by Min Liu (University of Texas at Austin), Jaejin Lee (Seoul National University), Jina Kang(University of Texas at Austin), and Sa Liu (University of Texas at Austin), investigates students behavio
15、ur patterns in interacting with a serious game environment using data visualization in order to understand how the patterns may vary according to students learning characteristics. 我们可以从数据中学习：利用一个严肃的游戏，由不同 性格的学生组成一个多案例的研究行为模式。调查学生 的行为模式，在一个严肃的游戏环境中，利用可视化的 数据为了理解怎样的模型会产生多样化根据学生的学习 特点。 2 Contributors
16、to this Special Section 对这个领域有贡献的人（3） Florence Martin (University of North Carolina) and John C. Whitmer (Blackboard, Inc.) examine in Applying Learning Analytics to Investigate Timed Release in Online Learning, the extent to which the adaptive release feature affected student behaviour in the online environment and course performance. Florence Martin（北卡罗来纳大学-美国）和John C. Whitmer (Blackboard, Inc.？) ，测试应用学习
