An Ontological Model for Assessment Analytics

Azer Nouira, Lilia Cheniti-Belcadhi, Rafik Braham

2017

Abstract

Today, there is a growing interest in data and analytics in the learning environment resulting in a highly qualified research concerning models, methods, tools, technologies and analytics. This research area is referred to as learning analytics. Metadata becomes an important item in an e-learning system, many learning analytics models are currently developed. They use metadata to tag learning materials, learning resources and learning activities. In this paper, we firstly give a detailed injection of the existing learning analytics models in the literature. We particularly observed that there is a lack of models dedicated to conceive and analyze the assessment data. That is why our objective in this paper is to propose an assessment analytics model inspired by the Experience API data model. Hence, an assessment analytics ontology model is developed supporting the analytics of assessment data by tracking the assessment activities, assessment result and assessment context of the learner.

Paper Citation

in Harvard Style

Nouira A., Cheniti-Belcadhi L. and Braham R. (2017). An Ontological Model for Assessment Analytics . In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 243-251. DOI: 10.5220/0006284302430251

in Bibtex Style

@conference{webist17,
author={Azer Nouira and Lilia Cheniti-Belcadhi and Rafik Braham},
title={An Ontological Model for Assessment Analytics},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={243-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006284302430251},
isbn={978-989-758-246-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - An Ontological Model for Assessment Analytics
SN - 978-989-758-246-2
AU - Nouira A.
AU - Cheniti-Belcadhi L.
AU - Braham R.
PY - 2017
SP - 243
EP - 251
DO - 10.5220/0006284302430251