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Direct linkElise Kokenge; Laura B. Holyoke; Krista M. Soria; Leda Kobziar; Steven B. Daley-Laursen – Natural Sciences Education, 2025
Understanding attrition risks specific to online student populations is crucial for the long-term success of online programs. Online programs allow place-based working professionals access to education needed for professional development and career advancement. This study was conducted to determine if educational preparation, student…
Descriptors: Online Courses, Student Attrition, Environmental Education, Science Education
Andrea 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
