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Direct linkAndrea Zanellati; Stefano Pio Zingaro; Maurizio Gabbrielli – IEEE Transactions on Learning Technologies, 2024
Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…
Descriptors: Dropouts, Dropout Characteristics, Potential Dropouts, Artificial Intelligence
Download full textDe Silva, Liyanachchi Mahesha Harshani; Chounta, Irene-Angelica; Rodríguez-Triana, María Jesús; Roa, Eric Roldan; Gramberg, Anna; Valk, Aune – Journal of Learning Analytics, 2022
Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative…
Descriptors: College Students, Dropouts, Dropout Prevention, Data Analysis
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
