학술논문

Predictive Analysis for Student Retention by Using Neuro-Fuzzy Algorithm
Document Type
Conference
Source
2018 10th Computer Science and Electronic Engineering (CEEC) Computer Science and Electronic Engineering (CEEC), 2018 10th. :41-45 Sep, 2018
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Correlation
Data mining
Software engineering
Predictive models
Training
Prediction algorithms
Adaptive Network based Fuzzy Inference System
Educational Data Mining
Prediction
Student Retention
Language
Abstract
The proliferation of mining techniques from diverse field of computing has drawn its impact on Educational Data Mining. The field itself has gained conspicuous attention of researchers who are engaged in predicting the student attrition and retention rate in educational institution in order to higher their rank. To achieve same goal, we have performed our analysis by choosing Adaptive Network based Fuzzy Inference System (ANFIS) algorithm that proven capable of predicting the student attrition rate in a Private University of Pakistan. With the help of experiments, we became able to predict the attrition rate and obtained the correlation among 5 input parameters that helps the students to improve their grades and administration of the university to improve their retention rate in near future.