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Models and Mapping: Improving Student Success Using Predictive Models and Data Visualizations -- 203 -- Proceedings Paper


16:15 - 17:15 on Tuesday, 11 September 2012 in 2.219

The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35 percent of students never finish high school. The drop rate among certain groups is as high as 50 to 60 percent. At the college level in the US only 30 percent of students graduate from two-year colleges in three years or less and approximately 50 percent graduate from four-year colleges in five years or less. A basic challenge in delivering global education, therefore, is improving student success. By student success we mean improving retention, completion, and graduation rates. In this paper we describe a Student Success System (S3) that provides a holistic, analytical view of student academic progress. At the conference we will demonstrate S3. The core of S3 is a predictive modeling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualizations for reaching diagnostic insights and a case management tool for managing interventions. Powered by learning analytics, S3 is intended as an end-to-end solution for identifying at-risk students, understanding why they are at risk, designing interventions to mitigate that risk, and finally closing the feedback look by tracking the efficacy of the applied intervention.



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