ASSISTING HIGHER EDUCATION IN ASSESSING, PREDICTING, AND MANAGING ISSUES RELATED TO STUDENT SUCCESS: A WEB-BASED SOFTWARE USING DATA MINING AND QUALITY FUNCTION DEPLOYMENT
Amar Sahay, Karun Mehta · 2010
This research seeks to develop a software system to assist higher education in assessing and predicting key issues related to student success. The software uses several data mining algorithms and quality tool such as, quality function deployment to study and predict issues including but not limited to enrollment management, dropout rate, time to degree, and suggest ways to improve courses and programs. Data mining knowledge discovery and predictive modeling tools along with quality function deployment (QFD) are used to uncover and understand hidden patterns in vast databases to understand student related issues and suggest ways to improve them. This research aims at: (1) discovering knowledge from massive data present in database of higher educational institution based on data mining, (2) using that knowledge for: classification, categorization, estimation, and visualization, (3) using predictive models including multiple regression, non-linear and logistic regression, to predict the critical student issues, (4) using the quality function deployment to address the voice of the students and other stakeholders and determine a prioritized list of critical factors that need to be deployed to design, develop, and improve the courses and programs vital to student success.