2026-06-05T17:11:33.082Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:11964 2026-05-31T22:04:36Z 2

Leveraging Metaheuristic Optimized Classifier Exploitability to Detect and Understand Student Dropout 2025 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/11964 https://hrcak.srce.hr/file/482222 Ž. Spalević L. Stosic N. Bačanin DŽakula L. Jovanović F. Marković Education is crucial for social progress, fostering knowledge, skills, and cognitive development, and is closely linked to long-term economic growth. By enhancing the general knowledge and skills of the population, education fuels innovation and the adoption of new ideas, making human capital a key driver of economic development. A well-educated workforce significantly contributes to research and development, integrating innovations into production and promoting economic growth. Despite extensive research, maintaining high enrolment and low dropout rates remains challenging. The potential of artificial intelligence (AI) in addressing this issue is underexplored. This work aims to fill this gap by leveraging metaheuristic optimized deep neural networks to detect students at risk of dropping out. A modified version of the firefly algorithm (FA) is introduced specifically to meet the demands of this optimization. Additionally, explainable AI (XAI) techniques are employed to better understand the factors influencing student decisions, thereby aiding in the formulation of effective retention policies. The introduced methodology is evaluated on a realworld dataset, with the best models achieving an accuracy exceeding 82% for dropout detection. article 10.15516/cje.v27i1.5950 27 1 85 127 1848-5197 Croatian Journal of Education : Hrvatski časopis za odgoj i obrazovanje