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Proceedings Paper

Predicting student success using analytics in course learning management systems
Author(s): Mohammed M. Olama; Gautam Thakur; Allen W. McNair; Sreenivas R. Sukumar
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Paper 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 of the developed models and their predictive accuracy.

Paper Details

Date Published: 22 May 2014
PDF: 9 pages
Proc. SPIE 9122, Next-Generation Analyst II, 91220M (22 May 2014); doi: 10.1117/12.2050641
Show Author Affiliations
Mohammed M. Olama, Oak Ridge National Lab. (United States)
Gautam Thakur, Oak Ridge National Lab. (United States)
Allen W. McNair, Oak Ridge National Lab. (United States)
Sreenivas R. Sukumar, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 9122:
Next-Generation Analyst II
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)

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