BCC: Organizing Multi-Disciplinary Communities to Conduct Data-Intensive Research on Education and Learning

BCC：组织多学科社区开展教育和学习方面的数据密集型研究

• 批准号: 1338509
• 负责人: Erin Peters-Burton
• 金额: $ 53.84万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1338509&HistoricalAwards=false
• 关键词: BCC; Organizing; Multi; Disciplinary; Communities

The PIs of this project are building the capacity for research within the data-intensive environments of massive open online courses. The PIs are using as an organizing example the World Education Portals (WEPS) massive open online course (MOOC) that focuses on calculus and has been taught through Florida State University and the University of Helsinki for over 8 years. The PIs are building a community of researchers who are studying ways to improve student achievement measures, pedagogical aspects and the extensions to mobile platforms. A specific set of research foci for this community include the elements of design-based experimentation in MOOCs, motivational and affective aspects that can be studied within such environments, the development of new statistical and analytics methods, ethical issues that surround access to data elements, and economic aspects of MOOCs. Multiple workshops bring the community together, and Mendeley.com (an academic social network) is being used to coordinate the growing research agendas around MOOCs in the time between the workshops. The project also includes an invitational conference to organize participant papers for a set of proceedings and reports. This project builds on work that was funded as part of a suite of projects collaborating virtually with institutes of higher education in Finland. The project provides a vehicle to move the development and initial research on MOOCs to a wider community. The project leverages individuals who are experts in mathematics learning together with experts in analytics and educational data mining to provide a deep connection to learning in the MOOC virtual learning environments. A criticism of the learning analytics and educational data mining research agenda is that a focus of the deep learning about STEM content is not well represented in the research and development community. Too frequently, the focus is on the technical development of the infrastructure and the statistical processes that are necessary to analyze the large amounts of data. What has been missing from the research agenda is the focus on how students are learning in MOOCs and what aspects of the learning environments are supporting that learning. This study brings the connection of the study of the learning of mathematics into the analysis of the adequacy of the MOOC learning environment.

该项目的 PI 正在大规模开放在线课程的数据密集型环境中建设研究能力。 PI 使用世界教育门户网站 (WEPS) 大规模开放在线课程 (MOOC) 作为组织示例，该课程专注于微积分，并已通过佛罗里达州立大学和赫尔辛基大学教授了 8 年多。 PI 正在建立一个研究人员社区，他们正在研究改善学生成绩衡量、教学方面和移动平台扩展的方法。该社区的一组特定研究重点包括 MOOC 中基于设计的实验要素、可以在此类环境中研究的动机和情感方面、新统计和分析方法的开发、围绕数据元素访问的道德问题、 MOOC 的经济方面。多个研讨会将社区聚集在一起，Mendeley.com（学术社交网络）被用来在研讨会之间协调围绕 MOOC 不断增长的研究议程。该项目还包括一次邀请会议，为一系列会议记录和报告组织参与者论文。该项目建立在与芬兰高等教育机构虚拟合作的一系列项目的一部分的资助工作的基础上。该项目提供了一种工具，将 MOOC 的开发和初步研究推广到更广泛的社区。该项目利用数学学习专家以及分析和教育数据挖掘专家，为 MOOC 虚拟学习环境中的学习提供深入的联系。对学习分析和教育数据挖掘研究议程的批评是，关于 STEM 内容的深度学习的重点在研究和开发社区中没有得到很好的体现。通常，重点是分析大量数据所需的基础设施的技术开发和统计流程。研究议程中缺少的是对学生如何在 MOOC 中学习以及学习环境的哪些方面支持这种学习的关注。本研究将数学学习的研究与 MOOC 学习环境的充分性分析联系起来。