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Authors: Ieva Vitolina ; Atis Kapenieks and Ieva Grada

Affiliation: Riga Technical University, Kalku Street 1, Riga, LV-1658, Latvia

Keyword(s): e-Inclusion, Machine Learning, Predictive Analytic.

Abstract: e-Inclusion aims to provide the benefits of digital technology for every member of society. Digital skills and their meaningful use are a prerequisite for everyone to be e-included. The improvement of learning outputs of online and blended courses on digital skills is therefore an important aspect of ensuring an e-included society. Due to the use of learning management systems and their ability to collect data on students, different types of student data become available for analysis. We proposed the data-driven approach which uses student data and machine learning algorithms to predict learning outcomes. The goal of this article is to present the conceptual architecture and prototype of the e-inclusion prediction system which is based on a combination of several algorithms and uses a machine learning approach.

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Paper citation in several formats:
Vitolina, I.; Kapenieks, A. and Grada, I. (2021). Modeling the e-Inclusion Prediction System. In Proceedings of the 13th International Conference on Computer Supported Education - Volume 2: CSEDU; ISBN 978-989-758-502-9; ISSN 2184-5026, SciTePress, pages 258-265. DOI: 10.5220/0010458302580265

@conference{csedu21,
author={Ieva Vitolina. and Atis Kapenieks. and Ieva Grada.},
title={Modeling the e-Inclusion Prediction System},
booktitle={Proceedings of the 13th International Conference on Computer Supported Education - Volume 2: CSEDU},
year={2021},
pages={258-265},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010458302580265},
isbn={978-989-758-502-9},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Computer Supported Education - Volume 2: CSEDU
TI - Modeling the e-Inclusion Prediction System
SN - 978-989-758-502-9
IS - 2184-5026
AU - Vitolina, I.
AU - Kapenieks, A.
AU - Grada, I.
PY - 2021
SP - 258
EP - 265
DO - 10.5220/0010458302580265
PB - SciTePress