doi: 10.18178/ijiet.2024.14.10.2163
Data-Driven Early Academic Intervention: Harnessing AI for Students Achievement
Fidel Cacheda*
,
Manuel F. López-Vizcaíno ,
Diego Fernández ,
Víctor Carneiro
- Center for Information and Communications Technologies Research (CITIC), Department of Computer Science and Information Technologies, University of A Coruña, A Coruña, Spain
- Manuscript receivedApril 1, 2024
- revisedMay 12, 2024
- acceptedJune 2, 2024
- publishedOctober 12, 2024
Abstract
In the dynamic landscape of higher education, the timely identification and mitigation of factors contributing to academic failure among university students are paramount for fostering academic success and student well-being. This research follows a quantitative research method using machine learning algorithms and strategically designed features extracted from students’ laboratory practices and questionnaires, to predict students’ academic performance. The primary motivation driving this research is to develop a model capable of identifying students at potential academic risk at mid-course, thereby enabling timely intervention strategies. Changes in the evaluation of laboratory practices are introduced to enhance the model’s predictive accuracy. Results demonstrate the model’s effectiveness in predicting final exam outcomes, achieving over 90% accuracy at the end of the course. A mid-course identification experiment shows the feasibility of predicting student outcomes with an accuracy exceeding 85%. The findings suggest the potential for early intervention strategies to improve student success.
Keywords
- academic failure
- artificial intelligence
- machine learning
- early detection
- data-driven
How to Cite
Fidel Cacheda, Manuel F. López-Vizcaíno, Diego Fernández, and Víctor Carneiro, "Data-Driven Early Academic Intervention: Harnessing AI for Students Achievement," International Journal of Information and Education Technology, vol. 14, no. 10, pp. 1328-1334, 2024. https://doi.org/10.18178/ijiet.2024.14.10.2163
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).