skip to main content

Title: Predicting Student Success using Analytics in Course Learning Management Systems

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 » the developed models and their predictive accuracy.« less
 [1] ;  [1] ;  [1] ;  [1]
  1. ORNL
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: SPIE Defense, Security, and Sensing, Baltimore, MD, USA, 20140505, 20140509
Research Org:
Oak Ridge National Laboratory (ORNL)
Sponsoring Org:
ME USDOE - Office of Management, Budget, and Evaluation; ORNL work for others
Country of Publication:
United States
Educational data mining; predictive analytics; learning management systems; logistic regression; feed-forward neural network