Uncovering Student Engagement and Performance in Applied AI in Finance: A Learning Analytics Approach

Neslihan Ademi, Aleksandra Porjazoska Kujundziski, Damir Rahmani

2025

Abstract

The product of the Erasmus+ project, Transversal Skills in Applied Artificial Intelligence (TSAAI), is the educational framework FuturIA, which offers a massive open online course focusing on the development of highly demanded transversal skills. The platform utilizes a unique pedagogical approach centered on solution-practice triplets and personalized learning pathways, aiming to adapt to individual student needs and foster effective skill acquisition. The TSAAI expert course was piloted by 30 students from universities participating in the project, enabling the consortium to refine the curriculum and teaching methodologies before the official launch of FuturIA. This study focuses on assessing the learning analytics of students, including descriptive analysis of log data, correlations between grades and course activities, clustering and a gender-based comparison of students’ success and engagement.

Paper Citation

in Harvard Style

Ademi N., Porjazoska Kujundziski A. and Rahmani D. (2025). Uncovering Student Engagement and Performance in Applied AI in Finance: A Learning Analytics Approach. In Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS; ISBN 978-989-758-783-2, SciTePress, pages 249-256. DOI: 10.5220/0014309800004848

in Bibtex Style

@conference{iceeecs25,
author={Neslihan Ademi and Aleksandra Porjazoska Kujundziski and Damir Rahmani},
title={Uncovering Student Engagement and Performance in Applied AI in Finance: A Learning Analytics Approach},
booktitle={Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS},
year={2025},
pages={249-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014309800004848},
isbn={978-989-758-783-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Advances in Electrical, Electronics, Energy, and Computer Sciences - Volume 1: ICEEECS
TI - Uncovering Student Engagement and Performance in Applied AI in Finance: A Learning Analytics Approach
SN - 978-989-758-783-2
AU - Ademi N.
AU - Porjazoska Kujundziski A.
AU - Rahmani D.
PY - 2025
SP - 249
EP - 256
DO - 10.5220/0014309800004848
PB - SciTePress