Using Big Data and Visual Analytics to Investigate the Long-term, Cascading Effects of Informal STEM Learning

使用大数据和可视化分析来调查非正式 STEM 学习的长期连锁效应

• 批准号: 1515550
• 负责人: John Falk
• 金额: $ 30万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1515550&HistoricalAwards=false
• 关键词: Using; Big; Data; Visual; Analytics

In concert with the overall strategy of the Advancing Informal STEM Learning (AISL) program to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments, Principal Investigators from Oregon State University, University of Idaho, and University of Texas at Dallas, will study a range of data in online social networks to identify evidence of the long-term impact of informal STEM education. Tracking informal learners over time to understand the impact of informal learning experiences has been a longstanding, daunting, and elusive challenge. Now, with massive amounts of data being shared and stored online, education researchers have an unprecedented opportunity to study such data and apply data analytics and visualization technologies to identify the long-term, cascading effects of informal STEM learning. Research findings will inform the design and development of a data-analysis tool for use by education practitioners to improve STEM learning experiences online, through television and film, and at informal education institutions. An independent external critical review board of learning scientists, computer scientists, engineers, informal STEM education practitioners, participating partners, broadcast media professionals, and policymakers, will ensure a robust evaluation of the research and effectiveness and utility of the data analysis tool to improve practice. A summary report for the field will be written on the scientific and practical reliability and validity of the research and data-analysis model, and the value of the work for audiences beyond informal STEM education practitioners and policymakers. The research is contemporaneously relevant, advancing innovative use of data-mining and data-analysis processes to better understand how informal learners communicate STEM learning experiences and interact with STEM content over time, across a range of social networks. Investigators will research: 1) whether learners who engage in informal STEM education experiences further their learning through discussions and sharing of information in social media networks, 2) which types of data are present in social media that are relevant for understanding the cascading impacts of learning over time, and 3) how learning may evolve independently within shared social networks, which, if discovered, could provide a predictive computational model with implications for significant impact across both formal and informal education. Investigators will employ existing and modified data crawlers to search for key terms and phrases, assess spikes and deformations in posts, queries, and blogs, and experiment with their test data to find which types or configurations of keywords or search terms deliver the most reliable and accurate results. A variety of formats will be explored to test various strategies with participating partners and practitioners. Data will be visualized to represent the following dimensions of learning: a) Interest/Affect, b) Recommendations, c) Understanding/Knowledge-Seeking, and d) Deeper Engagement.

与推进非正式STEM学习(AISL)计划的总体战略一致，以推进非正式环境中STEM学习的设计和开发的新方法和基于证据的理解，来自俄勒冈州立大学、爱达荷大学和达拉斯德克萨斯大学的首席调查人员将研究在线社交网络中的一系列数据，以确定非正式STEM教育长期影响的证据。随着时间的推移，跟踪非正式学习者以了解非正式学习经历的影响一直是一项长期、令人生畏和难以捉摸的挑战。现在，随着大量数据在网上共享和存储，教育研究人员有了一个前所未有的机会来研究这些数据，并应用数据分析和可视化技术来确定非正式STEM学习的长期连锁影响。研究结果将为设计和开发数据分析工具提供信息，供教育从业者使用，以改善在线、通过电视和电影以及在非正规教育机构的STEM学习体验。由学习科学家、计算机科学家、工程师、非正式STEM教育从业人员、参与伙伴、广播媒体专业人员和政策制定者组成的独立外部批评审查委员会将确保对数据分析工具的研究以及改进实践的有效性和实用性进行强有力的评估。将为实地编写一份总结报告，说明研究和数据分析模式的科学和实用可靠性和有效性，以及这项工作对非正式STEM教育从业者和政策制定者以外的受众的价值。这项研究具有时代意义，推动了数据挖掘和数据分析过程的创新使用，以更好地了解非正式学习者如何在一系列社交网络中随着时间的推移交流STEM学习经验并与STEM内容互动。调查人员将研究：1)参加非正式STEM教育的学习者是否经历了通过在社交媒体网络中讨论和共享信息来进一步学习的经历；2)社交媒体中存在哪些类型的数据，这些数据与理解学习随时间的级联影响相关；以及3)学习如何在共享的社交网络中独立发展，如果被发现，可以提供一个预测性计算模型，对正规和非正规教育产生重大影响。调查人员将使用现有的和修改后的数据爬虫来搜索关键术语和短语，评估帖子、查询和博客中的尖峰和变形，并使用他们的测试数据进行试验，以找出哪些类型或配置的关键字或搜索术语提供最可靠和准确的结果。将探索各种形式，与参与的合作伙伴和从业者一起测试各种战略。数据将被可视化，以代表学习的以下方面：a)兴趣/影响，b)建议，c)理解/寻求知识，以及d)更深层次的参与。