BIGDATA: Collaborative Research: F: Study of a Cyber-Enabled Social Computing Framework for Improving Practice in Online Computing Communities

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

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 through the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental understanding of key questions in the field, and catalyze the use of data science in Education Research. Computing has become an integral part of the practice of in modern science, technology engineering, and mathematics (STEM) fields. As a result, the STEM+Computing Partnership (STEM+C) program seeks to integrate the use of computational approaches in STEM teaching and learning and understand how this integration can improve STEM learning, engagement, and persistence. In this proposal, the Principal Investigators (PIs) will examine environments that many people engage in independently to learn computing, online communities and massive open online courses (MOOCs). This activity could be very compelling as people come to these environments because they have personal goals to learn the material. However, a challenge in these environments is that there is little support for learning. In addition, these environments are not adaptive to learners' needs. This project will tackle both of these challenges. The PIs will first characterize groups of learners to understand their needs and then design approaches to personalizing the environments based on those needs. The PIs address a need for both learning and independent online work communities by providing a combined learning work community. Many authentic production communitites, such as GitHub, do not provide support for novices who want to learn to contribute. Similarly many learning communities, such as MOOCs and other online learning environments, do not provide outlets for learners' products to become authentic. The PIs will combine data from MOOCs and online communities to discover groups of participants who behave in similar ways and investigate how to support the needs of these groups. This will lead to the proposed novel combined learning and work community that both provides support and offers authentic outlets for work products that are valued beyond a particular course.