adult education anonymization Assessment communities of practice Digital Educational content distance education Distance Learning E-commerce E-learning empirical research higher education Learning Analytics learning objects learning outcomes Lifecycle Model Lifelong learning MOOC Moodle ontologies Ontology Open and Distance Learning Personalized Learning Scratch εξ αποστάσεως εκπαίδευση οντολογίες
2019 |
Kyritsi, Kyriaki H; Zorkadis, Vassilios; Stavropoulos, Elias C; Verykios, Vassilios S The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy Inproceedings Journal of Interactive Media in Education, pp. 1–10, 2019. Abstract | Links | BibTeX | Ετικέτες: anonymization, data publishing, Distance Learning, Learning Analytics, privacy, statistical disclosure control @inproceedings{Verykios2019b, title = {The Pursuit of Patterns in Educational Data Mining as a Threat to Student Privacy}, author = {Kyriaki H. Kyritsi and Vassilios Zorkadis and Elias C. Stavropoulos and Vassilios S. Verykios}, url = {https://eeyem.eap.gr/wp-content/uploads/2019/12/502-4024-1-PB.pdf}, doi = {http://doi.org/10.5334/jime.502}, year = {2019}, date = {2019-05-27}, booktitle = {Journal of Interactive Media in Education}, volume = {1}, number = {2}, pages = {1–10}, abstract = {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}, keywords = {anonymization, data publishing, Distance Learning, Learning Analytics, privacy, statistical disclosure control}, pubstate = {published}, tppubtype = {inproceedings} } 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 |
2018 |
Kyritsi, Kyriaki; Zorkadis, Vasilios; Elias C. Stavropoulos, ; Verykios, Vassilios S Privacy Issues in Learning Analytics Conference Online, Open and Flexible Higher Education Conference 2018 (EADTU annual conference 2018), Aarhus Denmark, 2018, ISBN: 978-90-79730-35-3. Abstract | Links | BibTeX | Ετικέτες: anonymization, data publishing, Distance Learning, ethical issues, Learning Analytics, privacy, statistical disclosure control @conference{Kyritsi2018, title = {Privacy Issues in Learning Analytics}, author = {Kyriaki Kyritsi and Vasilios Zorkadis and Elias C. Stavropoulos, and Vassilios S. Verykios}, editor = {George Ubachs and Lizzie Konings}, url = {https://eeyem.eap.gr/wp-content/uploads/2019/12/Kyritsi_ProceedingsOOFHEC2018.pdf}, isbn = {978-90-79730-35-3}, year = {2018}, date = {2018-10-10}, booktitle = { Online, Open and Flexible Higher Education Conference 2018 (EADTU annual conference 2018)}, pages = {218-232}, address = {Aarhus Denmark}, abstract = {Today’s technological advances have led to tremendous advances in collecting, storing and analyzing data that come from diverse sources of information and may be represented in a wide variety of different formats (texts, photos, videos and many more). The term which perfectly describes this milestone in the history of computing is called big data and the task of analyzing these enormous amounts of data is called big data analytics. Academic institutions which offer open and distance learning programs such as the Hellenic Open University can profit from big data and the use of big data analytics by integrating it in their organizational support systems thus reflecting on their overall performance and planning competitive and attractive educational programs as well as improving in the delivery of their services. In the individual level, the modern lifestyle with the numerous networked devices and applications implies that all activities we are engaged in leave behind an imprint or a digital footprint. Combining thoroughly this variety of information creates a unique social genome (Kum et. al., 2014) for each and every one of us and understanding how to interpret it will bring major breakthroughs in many areas of our society such as improvements in social services, health and education. On the flip side, there are certain disadvantages concerning privacy issues arising from the inappropriate and illegitimate use of data containing personal information and ethical concerns about the basic right of individuals to have control over the amount and the extent of information they are willing to share. The systematic solution to this controversy is the design, application and evaluation of privacy-preserving data publishing methods in order to assure that the confidentiality of the subjects of projects is not compromised and a balance between the utility of the data and the privacy is accomplished.}, keywords = {anonymization, data publishing, Distance Learning, ethical issues, Learning Analytics, privacy, statistical disclosure control}, pubstate = {published}, tppubtype = {conference} } Today’s technological advances have led to tremendous advances in collecting, storing and analyzing data that come from diverse sources of information and may be represented in a wide variety of different formats (texts, photos, videos and many more). The term which perfectly describes this milestone in the history of computing is called big data and the task of analyzing these enormous amounts of data is called big data analytics. Academic institutions which offer open and distance learning programs such as the Hellenic Open University can profit from big data and the use of big data analytics by integrating it in their organizational support systems thus reflecting on their overall performance and planning competitive and attractive educational programs as well as improving in the delivery of their services. In the individual level, the modern lifestyle with the numerous networked devices and applications implies that all activities we are engaged in leave behind an imprint or a digital footprint. Combining thoroughly this variety of information creates a unique social genome (Kum et. al., 2014) for each and every one of us and understanding how to interpret it will bring major breakthroughs in many areas of our society such as improvements in social services, health and education. On the flip side, there are certain disadvantages concerning privacy issues arising from the inappropriate and illegitimate use of data containing personal information and ethical concerns about the basic right of individuals to have control over the amount and the extent of information they are willing to share. The systematic solution to this controversy is the design, application and evaluation of privacy-preserving data publishing methods in order to assure that the confidentiality of the subjects of projects is not compromised and a balance between the utility of the data and the privacy is accomplished. |