Erman Yukselturk

Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

Abstract This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.

Exploring the Link among Entry Characteristics, Participation Behaviors and Course Outcomes of Online Learners: An Examination of Learner Profile Using Cluster Analysis.

Online learner profiles have diversified such that now, they include all types of participants from a variety of backgrounds. This study analyzed online learner profiles in regard to their entry characteristics, participation behaviors and achievement of course outcomes. The sample consisted of 186 participants from an online course that required the employment of synchronous and asynchronous communication methods over the Internet. The data were collected through five online questionnaires and included 10 variables (gender, age, work status, self-efficacy, online readiness, self-regulation, participation in discussion list, participation in chat sessions, satisfaction and achievement). A two-step cluster analysis, chi-square analysis and multivariate analysis were used to analyze the collected data. The results indicated that online learners fell into three significantly different clusters and within each cluster, learners had a close combination of employment characteristics, gender type, age level, perception of self-efficacy for online technologies and participation behavior level. [ABSTRACT FROM AUTHOR]