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.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. Keywords: predictive models; data visualisation; student performance; risk analytics (Published: 30 August 2012) http://dx.doi.org/10.3402/rlt.v20i0.19191
Description
Improving student success using predictive models and data visualisations | Essa | Research in Learning Technology
%0 Journal Article
%1 essa2012improving
%A Essa, Alfred
%A Ayad, Hanan
%D 2012
%K learning-analytics retention
%T Improving student success using predictive models and data visualisations
%U http://www.researchinlearningtechnology.net/index.php/rlt/article/view/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 50–60%. 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.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. Keywords: predictive models; data visualisation; student performance; risk analytics (Published: 30 August 2012) http://dx.doi.org/10.3402/rlt.v20i0.19191
@article{essa2012improving,
abstract = {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.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. Keywords: predictive models; data visualisation; student performance; risk analytics (Published: 30 August 2012) http://dx.doi.org/10.3402/rlt.v20i0.19191 },
added-at = {2015-08-29T18:41:15.000+0200},
author = {Essa, Alfred and Ayad, Hanan},
biburl = {https://www.bibsonomy.org/bibtex/2846451a026c7b96d0f26fded4802cdef/jennymac},
description = {Improving student success using predictive models and data visualisations | Essa | Research in Learning Technology},
id = {19191, 10.3402/rlt.v20i0.19191, http://www.researchinlearningtechnology.net/index.php/rlt/article/view/19191},
interhash = {2513224a6e0ab8e3504ce9a053137665},
intrahash = {846451a026c7b96d0f26fded4802cdef},
keywords = {learning-analytics retention},
timestamp = {2015-08-29T18:41:15.000+0200},
title = {Improving student success using predictive models and data visualisations},
type = {Text.Serial.Journal},
url = {http://www.researchinlearningtechnology.net/index.php/rlt/article/view/19191},
year = 2012
}