Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations

Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decisi...

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Veröffentlicht in:	IEEE transactions on learning technologies 2019-04, Vol.12 (2), p.198-211
Hauptverfasser:	Cano, Alberto, Leonard, John D.
Format:	Artikel
Sprache:	eng
Schlagworte:	Academic Achievement Alarm systems At Risk Students Classification College Students Colleges & universities Computer Assisted Instruction Data Collection Data mining Data models Decision making Demographics Disproportionate Representation Dropout Prevention early prediction Early warning systems Education Educational data mining Feedback (Response) Genetic algorithms Genetic programming Higher education institutions Information sources Learning Mathematics multi-view learning Populations Progress Monitoring Repositories Sociology Statistical analysis Statistics Student Characteristics Student Needs student performance Student Records Students Teachers Underachievement Universities
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