BIGDATA: EAGER: Infrastructure and Analytics for Data Intensive Research in Open-Ended Learning Environments

BIGDATA：开放式学习环境中数据密集型研究的基础设施和分析数据科学技术已经彻底改变了许多学术领域，并在商业领域取得了巨大的成就。迄今为止，它们在解决美国教育系统中的关键问题方面没有得到充分利用，特别是在理解科学，技术，工程和数学（STEM）学习和学习环境，扩大STEM的参与，以及提高传统上STEM服务不足的学生的保留率方面。教育和人力资源局（EHR）的目标是通过EHR核心研究计划，推进大数据科学工程（BIGDATA）计划的基础和应用的关键技术和技术，以推进旨在理解和解决这些关键问题的基础研究，并促进数据科学在教育研究中的应用。&随着越来越多的开放式学习环境在学校中被采用，了解它们如何影响STEM的学习，动机和参与至关重要。教育研究中最广泛使用的方法不足以解决这些问题，因为它们没有解决这些环境提供的数据的规模和复杂性。探索性研究早期概念补助金（EAGER）将通过开发数据存储库和开源分析工具，促进对有前途的新技术环境如何影响这些成果的理解。PI将通过使用这些工具以及来自三种不同类型的在线学习环境的数据来证明概念。该提案的主要目标是开发一个开放式学习环境（OELE）教育数据集存储库和新的集成和分析技术，挖掘理论驱动和自下而上的数据挖掘的潜力，以理解OELE中的学习。 此外，首席研究员将开发一个分析环境，其中包括共同的工具，以支持研究人员和从业人员进行分析。拟议的数据集成挑战是雄心勃勃的，也是有风险的。然而，PI拥有完成项目的专业知识。在开放式学习环境中探索数据科学方法，以及使用自下而上的方法进行数据分析，都是教育研究中非常需要的。大多数基于计算机的学习环境比那些在教育数据挖掘和学习分析中使用的大多数方法都更加开放。自下而上的数据挖掘方法的使用在商业部门和生物学等学术领域产生了惊人的结果。目前在学术环境中的教育领域几乎没有其他类似的努力，而且商业领域的大多数项目都不是开源项目。该奖项由EHR核心研究（ECR）计划支持。 ECR计划强调基础STEM教育研究，产生该领域的基础知识。 投资是在关键领域是必不可少的，广泛的和持久的：干学习和干学习环境，扩大参与干，干劳动力发展。

BIGDATA: Infrastructure and Analytics for Data Intensive Research in Open-Ended Learning EnvironmentsData science techniques have revolutionized many academic fields and led to terrific gains in the commercial sector. They have to date been underutilized in solving critical problems in the US educational system, particularly in understanding Science, Technology, Engineering and Mathematics (STEM) learning and learning environments, broadening participation in STEM, and increasing retention for students traditionally underserved in STEM. The goals of the Directorate for Education and Human Resources (EHR), through the EHR Core Research program, for the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental research aimed at understanding and solving these critical problems, and to catalyze the use of data science in Education Research. As more open-ended learning environments are being employed in schools, it is critical to understand how they affect learning, motivation and engagement in STEM. The most widely used methods in educational research are inadequate for addressing these questions because they do not address the scale and complexity of the data provided from these environments. This Early Concept Grant for Exploratory Research (EAGER) will advance the understanding of how promising new technology environments affect these outcomes by developing a data repository and open-source analytical tools. The PI will show proof of concept by using these tools with data from three different types of online learning environments. The main goal of the proposal is to develop an open-ended learning environment (OELE) educational dataset repository and novel integration and analysis techniques that tap the potential of both theory driven and bottom-up data mining for understanding learning in OELEs. In addition, the Principal Investigator will develop an analysis environment incorporating common tools to support researchers and practitioners in conducting analyses. The proposed data integration challenge is ambitious and risky. However, the PI has the expertise to complete the project. Both the exploration of data science methods in open-ended learning environments and of the use of bottom up approaches to data analysis are greatly needed in educational research. Most computer based learning environments are more open ended than those for which the majority of the methodologies used in educational data mining and learning analytics have been developed. The use of bottom up methods of data mining has produced phenomenal results in the commercial sector and in academic fields such as biology. There are almost no other similar efforts currently underway in education in the academic setting, and most in the commercial sector are not open source projects.This award is supported by the EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.