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

Vanderbilt University, Nashville TN

Investigators

Abstract

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