Predicting Student Success using Analytics in Course Learning Management Systems
Abstract
Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance ofmore »
- Authors:
-
- ORNL
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE; Work for Others (WFO)
- OSTI Identifier:
- 1129611
- DOE Contract Number:
- DE-AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: SPIE Defense, Security, and Sensing, Baltimore, MD, USA, 20140505, 20140509
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Educational data mining; predictive analytics; learning management systems; logistic regression; feed-forward neural network
Citation Formats
Olama, Mohammed M, Thakur, Gautam, McNair, Wade, and Sukumar, Sreenivas R. Predicting Student Success using Analytics in Course Learning Management Systems. United States: N. p., 2014.
Web.
Olama, Mohammed M, Thakur, Gautam, McNair, Wade, & Sukumar, Sreenivas R. Predicting Student Success using Analytics in Course Learning Management Systems. United States.
Olama, Mohammed M, Thakur, Gautam, McNair, Wade, and Sukumar, Sreenivas R. 2014.
"Predicting Student Success using Analytics in Course Learning Management Systems". United States.
@article{osti_1129611,
title = {Predicting Student Success using Analytics in Course Learning Management Systems},
author = {Olama, Mohammed M and Thakur, Gautam and McNair, Wade and Sukumar, Sreenivas R},
abstractNote = {Educational data analytics is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. For example, predicting college student performance is crucial for both the student and educational institutions. It can support timely intervention to prevent students from failing a course, increasing efficacy of advising functions, and improving course completion rate. In this paper, we present the efforts carried out at Oak Ridge National Laboratory (ORNL) toward conducting predictive analytics to academic data collected from 2009 through 2013 and available in one of the most commonly used learning management systems, called Moodle. First, we have identified the data features useful for predicting student outcomes such as students scores in homework assignments, quizzes, exams, in addition to their activities in discussion forums and their total GPA at the same term they enrolled in the course. Then, Logistic Regression and Neural Network predictive models are used to identify students as early as possible that are in danger of failing the course they are currently enrolled in. These models compute the likelihood of any given student failing (or passing) the current course. Numerical results are presented to evaluate and compare the performance of the developed models and their predictive accuracy.},
doi = {},
url = {https://www.osti.gov/biblio/1129611},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 00:00:00 EST 2014},
month = {Wed Jan 01 00:00:00 EST 2014}
}