%0 Journal Article
%T Improving student success using predictive models and data visualisations
%A Alfred Essa
%A Hanan Ayad
%J Research in Learning Technology
%D 2012
%I Co-Action Publishing
%R 10.3402/rlt.v20i0.19191
%X 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% of students never finish high school. The drop rate among some demographic groups is as high as 50¨C60%. At the college level in the US only 30% of students graduate from 2-year colleges in 3 years or less and approximately 50% graduate from 4-year colleges in 5 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.1 The core of S3 is a flexible predictive modelling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualisations for reaching diagnostic insights and a case management tool for managing interventions. S3's open modular architecture will also allow integration and plug-ins with both open and proprietary software. 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.
%K predictive models
%K data visualisation
%K student performance
%K risk analytics
%U http://www.researchinlearningtechnology.net/index.php/rlt/article/view/19191/pdf_1