Data Mining in Higher Education : University Student Dropout Case Study

摘要

In this paper, we apply different data mining approaches for the purpose of examining and predictingstudents’ dropouts through their university programs. For the subject of the study we select a total of 1290records of computer science students Graduated from ALAQSA University between 2005 and 2011. Thecollected data included student study history and transcript for courses taught in the first two years ofcomputer science major in addition to student GPA , high school average , and class label of (yes ,No) toindicate whether the student graduated from the chosen major or not. In order to classify and predictdropout students, different classifiers have been trained on our data sets including Decision Tree (DT),Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlBclassifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationshipsbetween student dropout status and enrolment persistence by mining a frequent cases using FP-growthalgorithm.