Data Storytelling in Learning Analytics: AI-Driven Competence Assessment

Ainhoa Álvarez, Aitor Renobales-Irusta, Mikel Villamañe

2025

Abstract

Learning dashboards have become very popular, but the information shown on them is often difficult to interpret by users. Different authors have worked to improve dashboards including narratives or data storytelling techniques. However, creating these narratives is a complex process. Several studies have begun to analyse the use of GenAI tools to generate these narratives in a scalable way, but this is still an area of study that is at an early stage. In this paper, we present a proposal and a study aimed at generating narratives using GenAI, extending previous work by aligning the generated narratives with the curriculum design of the course. We first present a proposal for generating the narratives and then a study to evaluate their adequacy.

Paper Citation

in Harvard Style

Álvarez A., Renobales-Irusta A. and Villamañe M. (2025). Data Storytelling in Learning Analytics: AI-Driven Competence Assessment. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 536-543. DOI: 10.5220/0013567300003967

in Bibtex Style

@conference{data25,
author={Ainhoa Álvarez and Aitor Renobales-Irusta and Mikel Villamañe},
title={Data Storytelling in Learning Analytics: AI-Driven Competence Assessment},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={536-543},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013567300003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Data Storytelling in Learning Analytics: AI-Driven Competence Assessment
SN - 978-989-758-758-0
AU - Álvarez A.
AU - Renobales-Irusta A.
AU - Villamañe M.
PY - 2025
SP - 536
EP - 543
DO - 10.5220/0013567300003967
PB - SciTePress