About this paper

Appears in:
Pages: 8708-8715
Publication year: 2017
ISBN: 978-84-697-6957-7
ISSN: 2340-1095
doi: 10.21125/iceri.2017.2387

Conference name: 10th annual International Conference of Education, Research and Innovation
Dates: 16-18 November, 2017
Location: Seville, Spain


A. Giannakoulopoulos1, A. Kouretsis2, L. Limniati3

1Ionian University (GREECE)
2National and Kapodistrian University of Athens (GREECE)
3University of Helsinki (FINLAND)
In the latest years learning analytics (LA) have been introduced as a field that seeks to provide answers to questions that are related to the education and the data which are gathered when the students are engaging in the Learning experience (Larusson & White, 2014). The reasons why using analytics in education has shown growth during the recent years is basically because of the increase in the data quantity, the improved data formats, the advances in computing, and the availability of more sophisticated tools for analytics (Baker, & Inventado, 2014). Undoubtedly, learning analytics can be a useful tool for learning and eLearning experiences. But each student has different needs. For this reason, we are suggesting of creating a tool which will be tailored to every student’s needs. This will happen by using simulated data in an elearning environment. A student profile is a set of attributes and their values which can describe the student. In order for this to happen, we will use two kinds of dataset. The static student dataset which will provide basic information about the student such as his age or his past academic performance. The second data set will be the dynamic one which will refer to the student’s performance, engagement, absences etc. By performing simulations, a lot of useful conclusions can be derived and by using these data we will come up with useful suggestions based on the personalized learning techniques.
author = {Giannakoulopoulos, A. and Kouretsis, A. and Limniati, L.},
series = {10th annual International Conference of Education, Research and Innovation},
booktitle = {ICERI2017 Proceedings},
isbn = {978-84-697-6957-7},
issn = {2340-1095},
doi = {10.21125/iceri.2017.2387},
url = {https://dx.doi.org/10.21125/iceri.2017.2387},
publisher = {IATED},
location = {Seville, Spain},
month = {16-18 November, 2017},
year = {2017},
pages = {8708-8715}}
AU - A. Giannakoulopoulos AU - A. Kouretsis AU - L. Limniati
SN - 978-84-697-6957-7/2340-1095
DO - 10.21125/iceri.2017.2387
PY - 2017
Y1 - 16-18 November, 2017
CI - Seville, Spain
JO - 10th annual International Conference of Education, Research and Innovation
JA - ICERI2017 Proceedings
SP - 8708
EP - 8715
ER -
A. Giannakoulopoulos, A. Kouretsis, L. Limniati (2017) LEARNING ANALYTICS FOR ELEARNING, ICERI2017 Proceedings, pp. 8708-8715.