@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}
}

@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}
}

@inproceedings{Kagklis2016,
title = {A Learning Analytics Methodology for Student Performance Assessment in a Distance and Open Education Environment},
author = {Vasileios Kagklis and Antonis Lionarakis and Elias C. Stavropoulos and Vassilios S. Verykios},
url = {http://eeyem.eap.gr/wp-content/uploads/2017/06/EADTU2016paper.pdf},
year = {2016},
date = {2016-10-19},
booktitle = {In Proc. of The Online, Open and Flexible Higher Education Conference 2016 (EADTU conference 2016)},
pages = {735-748},
keywords = {Big Data, distance education, Learning Analytics, Student Performance Assessment},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{423,
title = {A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education},
author = {Vasileios Kagklis and Anthi Karatrantou and Maria Tantoula and Chris T. Panagiotakopoulos and Vassilios S. Verykios},
issn = {1027-5207},
journal = {European Journal of Open, Distance and e-Learning},
volume = {18},
abstract = {

Online fora have become not only one of the most popular communication tools in e-learning environments, but also one of the key factors of the learning process, especially in distance learning, as they can provide to the students involved, motivation for collaboration in order to achieve a common goal. The purpose of this study is to analyse data related to the participation of postgraduate students in the online forum of their course at the Hellenic Open University. The content of the messages posted is analysed by using text mining techniques, while the network through which the students interact is processed through social network analysis techniques. Furthermore, sentiment analysis and opinion mining is applied on the same dataset. Our aim is to study studentstextquoteright attitude towards the course and its features, as well as to model their sentiment behaviour over time, and finally to detect if and how this affected their overall performance. The combined knowledge attained from the aforementioned techniques can provide tutors with practical and valuable information for the structure and the content of the studentstextquoteright exchanged messages, the patterns of interaction among them, the trend of sentiment polarity during the course, so as to improve the educational process.

},
keywords = {Educational Data Mining, Learning Analytics, Sentiment analysis, Social Network Analysis},
pubstate = {published},
tppubtype = {article}
}