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ERIC Number: EJ1003114
Record Type: Journal
Publication Date: 2012
Pages: 13
Abstractor: As Provided
Reference Count: 6
ISBN: N/A
ISSN: ISSN-2156-7069
Improving Student Success Using Predictive Models and Data Visualisations
Essa, Alfred; Ayad, Hanan
Research in Learning Technology, v20 suppl p58-70 2012
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 50-60%. 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. 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. (Contains 1 note.) [This paper was published in the ALT-C 2012 Conference Proceedings.]
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Publication Type: Journal Articles; Reports - Research; Speeches/Meeting Papers
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A