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Download full textJuan Andrés Talamás-Carvajal; Héctor G. Ceballos; Isabel Hilliger – Journal of Learning Analytics, 2025
Artificial intelligence (AI) is currently leading an industrial revolution in most aspects of human life, and education is no exception. With the increasing ratio of students to faculty, AI could be an extremely beneficial tool for individual mentoring; for example, for cases of dropout and for student retention. While many models have already…
Descriptors: Higher Education, Artificial Intelligence, Research Methodology, Student Subcultures
Direct linkLidia Rossi; Mara Soncin; Melisa Lucia Diaz Lema; Tommaso Agasisti – Educational Assessment, Evaluation and Accountability, 2025
Early identification of schools with a high percentage of students at risk of learning poverty is crucial for effective and targeted interventions. This study investigates the use of an innovative combination of large-scale administrative datasets and advanced statistical techniques to predict schools at risk of learning poverty in Italy in the…
Descriptors: Disadvantaged Schools, At Risk Students, Foreign Countries, Economically Disadvantaged
Thao-Trang Huynh-Cam; Long-Sheng Chen; Tzu-Chuen Lu – Journal of Applied Research in Higher Education, 2025
Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics
