Detecting Student Dropouts Using Fuzzy Inferencing

<p>Fuzzy logic provides a methodology for reasoning using imprecise rules and assertions. Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. This study concerns the development of a Fuzzy Inference System (FIS) for identifying likely student dropouts at Columbus State University (CSU). The fuzzy inference based model uses a hybrid knowledge extraction process to predict how likely each freshman student will be to drop their program of study at the end of their first semester. This process uses both a top down (symbolic) and a bottom-up (data-based) approach. Historical student records data have been used to evaluate the developed FIS. Findings of this study indicate that the FIS does not perform better than an Artificial Neural Network (ANN) developed for the same purpose, but useful insights about how different student attributes relate to their retention or departure may be gained from the rules that define the fuzzy model.</p>

Name thesis:degree_name

Computer Science - Applied Computing Track

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

TSYS School of Computer Science

Year dc:date.available

2013

Author dc:creator

• Husainy, Shahriar

Contributors dc:contributor

• Dr. Shamim Khan
• Dr. Lydia Ray
• Dr. Yesem K. Peker

• Fuzzy Logic
• Fuzzy Interference System (FIS)
• Columbus State University (CSU)
• Artificial Neural Network (ANN)
• Student Dropouts
• Computer Sciences
• Data Science
• Theory and Algorithms

Language dc:language

English

Repository record dc:identifier

https://csuepress.columbusstate.edu/theses_dissertations/33

OAI identifier oai:identifier

oai:csuepress.columbusstate.edu:theses_dissertations-1033