A Forecasting model to evaluate a freshman's ability to succeed by using particular full-scaled class association rules (PFSCARs)

Abstract

This study was to create a forecasting model for evaluate freshmen's ability to succeed with using the longest rules from CARs technique as called a particular full-scaled class association rules (PFSCARs). The purposed of this study was to create a classifier tool to evaluate freshmen's ability. This study used demographic data of students in Information Technology program Chandrakasem Rajabhat University and current grade levels of students as equalized levels. The equalized levels consist of 3 classes: good, fair and poor. The result of this study that proposed that the forecasting model to evaluate freshmen quality with PFSCARs performed at a good level of performance with an accuracy rate of 79% of the students equalized model. Finally, the research discovered that the forecasting model to evaluate freshmen quality can be a guideline for academic advisors or other relevant persons to help new students, to manage an appropriated study plan and could help them to improve course or curriculum in the future as well. © 2009 IEEE.