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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Husainy, Shahriar"],"institution":null,"degree_name":"Computer Science - Applied Computing Track","degree_level":"Thesis","degree_discipline":"TSYS School of Computer Science","degree_department":null,"school":null,"contributors":["Dr. Shamim Khan","Dr. Lydia Ray","Dr. Yesem K. Peker"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-01T07:00:00Z","date_published":"2013-05-01T07:00:00Z","updated_at":"2026-07-24T01:44:55Z","subjects":["Fuzzy Logic","Fuzzy Interference System (FIS)","Columbus State University (CSU)","Artificial Neural Network (ANN)","Student Dropouts","Computer Sciences","Data Science","Theory and Algorithms"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/33","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Shamim Khan","Dr. Lydia Ray","Dr. Yesem K. Peker"]},{"key":"dc:creator","label":"Author","values":["Husainy, Shahriar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2015-10-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["TSYS School of Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Computer Science - Applied Computing Track"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fuzzy Logic","Fuzzy Interference System (FIS)","Columbus State University (CSU)","Artificial Neural Network (ANN)","Student Dropouts","Computer Sciences","Data Science","Theory and Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/33"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Detecting Student Dropouts Using Fuzzy Inferencing"]}]}],"canonical_facts":{"dc:contributor":["Dr. Shamim Khan","Dr. Lydia Ray","Dr. Yesem K. Peker"],"dc:creator":["Husainy, Shahriar"],"dc:date.available":["2015-10-01T07:00:00Z"],"dc:description.abstract":["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.
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