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A decision support prototype tool for predicting student performance in an ODL environment

A decision support prototype tool for predicting student performance in an ODL environment Machine Learning algorithms fed with data sets which include information such as attendance data, test scores and other student information can provide tutors with powerful tools for decisionmaking. Until now, much of the research has been limited to the relation between single variables and student performance. Combining multiple variables as possible predictors of dropout has generally been overlooked. The aim of this work is to present a high level architecture and a case study for a prototype machine learning tool which can automatically recognize dropoutprone students in university level distance learning classes. Tracking student progress is a timeconsuming job which can be handled automatically by such a tool. While the tutors will still have an essential role in monitoring and evaluating student progress, the tool can compile the data required for reasonable and efficient monitoring. What is more, the application of the tool is not restricted to predicting dropout prone students it can be also used for the prediction of students marks, for the prediction of how many students will submit a written assignment, etc. It can also help tutors explore data and build models for prediction, forecasting and classification. Finally, the underlying architecture is independent of the data set and as such it can be used to develop other similar tools http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Interactive Technology and Smart Education Emerald Publishing

A decision support prototype tool for predicting student performance in an ODL environment

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1741-5659
DOI
10.1108/17415650480000027
Publisher site
See Article on Publisher Site

Abstract

Machine Learning algorithms fed with data sets which include information such as attendance data, test scores and other student information can provide tutors with powerful tools for decisionmaking. Until now, much of the research has been limited to the relation between single variables and student performance. Combining multiple variables as possible predictors of dropout has generally been overlooked. The aim of this work is to present a high level architecture and a case study for a prototype machine learning tool which can automatically recognize dropoutprone students in university level distance learning classes. Tracking student progress is a timeconsuming job which can be handled automatically by such a tool. While the tutors will still have an essential role in monitoring and evaluating student progress, the tool can compile the data required for reasonable and efficient monitoring. What is more, the application of the tool is not restricted to predicting dropout prone students it can be also used for the prediction of students marks, for the prediction of how many students will submit a written assignment, etc. It can also help tutors explore data and build models for prediction, forecasting and classification. Finally, the underlying architecture is independent of the data set and as such it can be used to develop other similar tools

Journal

Interactive Technology and Smart EducationEmerald Publishing

Published: Nov 30, 2004

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