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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 John Falk from Oregon State University, Hasan Jamil from University of Idaho, and Kang Zhang from 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.