Predicting Student Failure in an Online Course: Implications for Advisement
Abstract
Students like the idea of taking courses online because they consider e-education to be convenient and flexible. School administrators see online courses as a way to reduce costs. Online learning, however, is not for everyone. The drop rate for online classes is disturbingly large, mainly because many students find online classes to be harder than they expected. Additionally, online learning is basically foreign to most students. By the time many students take their first online course, they have taken over 100 traditional classes. Obviously, good student advisement concerning e-courses is necessary. In this session, we will discuss an empirical model to predict the probability of a student earning a grade in a specific online course that is in the bottom 25% of the class. Participants will be encouraged to explore with us the usefulness of our model as well as its applicability for other courses and/or at other schools.
Location
Room 1908
Publication Type and Release Option
Event
Recommended Citation
Fendler, Richard J.; Ruff, Craig; and Shrikhande, Milind, "Predicting Student Failure in an Online Course: Implications for Advisement" (2011). SoTL Commons Conference. 65.
https://digitalcommons.georgiasouthern.edu/sotlcommons/SoTL/2011/65
Predicting Student Failure in an Online Course: Implications for Advisement
Room 1908
Students like the idea of taking courses online because they consider e-education to be convenient and flexible. School administrators see online courses as a way to reduce costs. Online learning, however, is not for everyone. The drop rate for online classes is disturbingly large, mainly because many students find online classes to be harder than they expected. Additionally, online learning is basically foreign to most students. By the time many students take their first online course, they have taken over 100 traditional classes. Obviously, good student advisement concerning e-courses is necessary. In this session, we will discuss an empirical model to predict the probability of a student earning a grade in a specific online course that is in the bottom 25% of the class. Participants will be encouraged to explore with us the usefulness of our model as well as its applicability for other courses and/or at other schools.
