On The Accuracy and Completeness of The Record Matching Process

Abstract. Record matching or linking is one of the phases of the data quality improvement process, in which, records from different sources, are cleansed and integrated in a centralized data store to be used for various purposes. Both, earlier and recent studies in data quality and record linkage focus on various statistical models, which make strong assumptions on the probabilities of attribute errors. In this study, we evaluate different models for record linkage, which are built based on data only. We use a program that generates data with known error distributions and we train classification models, which we use to estimate the accuracy and the completeness of the record linking process. The results indicate that the automated learning techniques are adequate for this process and that both their accuracy and their completeness are comparable to the accuracy and the completeness of other, mostly manual, processes

Student Admission Data Analytics for Open and Distance Education in Greece

Over the last few decades, distance learning has become very popular, as a result of the many pros it offers, along with its flexibility. The need for a better understanding of the data originating from such educational environments has led to the rise of the Educational Data Mining research field. However, most of the studies so far focus on the analysis of the data being collected during and/or after the distance learning courses. In this paper, we study the demographical data related to student applications for acceptance in distance learning programs offered by the Hellenic Open University, during the decade from 2003 to 2013. Our study aims at analyzing the data, and discovering patterns and knowledge that can be used to help the strategic placement of the university, and the improvement of the experience that offers to its students. Moreover, we attempt to correlate the discovered findings with the social and financial status of the applicants’ environment