CCE Theses and Dissertations

Date of Award

2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computing Technology in Education (DCTE)

Department

College of Engineering and Computing

Advisor

Steven R. Terrell

Committee Member

Sumitra Mukherjee

Committee Member

Thomas McFarland

Keywords

analytical modeling, modeling, non-traditional students, on-line students, predicting at-risk students, retention, Educational technology, Computer science, Higher education

Abstract

Predictive statistical modeling shows promise in accurately predicting academic performance for students enrolled in online programs. This approach has proven effective in accurately identifying students who are at-risk enabling instructors to provide instructional intervention. While the potential benefits of statistical modeling is significant, implementations have proven to be complex, costly, and difficult to maintain. To address these issues, the purpose of this study is to develop a fully integrated, automated predictive modeling system (PMS) that is flexible, easy to use, and portable to identify students who are potentially at-risk for not succeeding in a course they are currently enrolled in. Dynamic and static variables from a student system (edX) will be analyzed to predict academic performance of an individual student or entire class. The PMS model framework will include development of an open-source Web application, application programming interface (API), and SQL reporting services (SSRS). The model is based on knowledge discovery database (KDD) approach utilizing inductive logic programming language (ILP) to analyze student data. This alternative approach for predicting academic performance has several unique advantages over current predictive modeling techniques in use and is a promising new direction in educational research.

Download
Share Feedback

Share

COinS