How Early Can We Identify At-Risk Students? An Analysis of LMS Interactions

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Detaylı Bibliyografya
Yayımlandı:	European Conference on e-Learning (Oct 2025), p. 88-97
Yazar:	de Silva, Tiloka
Diğer Yazarlar:	Sanjula, Yomashi
Baskı/Yayın Bilgisi:	Academic Conferences International Limited
Konular:	Modelling Early warning systems At risk students Learning management systems Machine learning Distance learning Decision trees Students Colleges & universities COVID-19 Performance measurement Support vector machines Education Undergraduate study Learning Modalities Learning Analytics Indexes Undergraduate Students Data Collection Distance Education Grouping (Instructional Purposes) Academic Records Academic Achievement Behavior Patterns Instructional Materials Interpersonal Competence Correlation Blended Learning Electronic Learning Artificial Intelligence In Person Learning Educational Environment Course Content School Holding Power Higher Education Algorithms
Online Erişim:	Citation/Abstract Full Text Full Text - PDF
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