The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy

• Published in: Journal of interactive media in education : JiME Vol. 2019; no. 1
• Main Authors: Kyritsi, Kyriaki H., Zorkadis, Vasilios, Stavropoulos, Elias C., Verykios, Vassilios S.
• Format: Journal Article
• Language: English
• Published: Institute of Educational Technology, The Open University 27.05.2019; Ubiquity Press
• Subjects: Access to Information; anonymization; College Students; Confidentiality; Data Collection; data publishing; Distance Education; distance learning; Educational Research; Foreign Countries; Information Dissemination; Information Management; Information Security; learning analytics; Open Universities; Privacy; statistical disclosure control; Student Records; Greece
• ISSN: 1365-893X; 1365-893X
• DOI: 10.5334/jime.502

Recent technological advances have led to tremendous capacities for collecting, storing and analyzing data being created at an ever-increasing speed from diverse sources. Academic institutions which offer open and distance learning programs, such as the Hellenic Open University, can benefit from big data relating to its students' information and communication systems and the use of modern techniques and tools of big data analytics provided that the student's right to privacy is not compromised. The balance between data mining and maintaining privacy can be reached through anonymisation methods but on the other hand this approach raises technical problems such as the loss of a certain amount of information found in the original data. Considering the learning process as a framework of interacting roles and factors, the discovery of patterns in that system can be really useful and beneficial firstly for the learners and furthermore, the ability to publish and share these results would be very helpful for the whole academic institution.