A machine learning framework to identify students at risk of adverse academic outcomes

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2015-August, Issue , 2015, Pages 1909-1918

  Lakkaraju, Himabindu ^a   Aguiar, Everaldo ^b   Shan, Carl ^c   Miller, David ^d   Bhanpuri, Nasir ^c   Ghani, Rayid ^c   Addison, Kecia L ^e  

^a STANFORD UNIVERSITY   (United States)

^b UNIVERSITY OF NOTRE DAME   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

^e Montgomery County Public Schools ^*  (United States)

Author keywords

Applications; Education; Evaluation metrics; Risk prediction

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; DATA MINING; EDUCATION; LEARNING SYSTEMS; STUDENTS;

CLASSIFICATION ALGORITHM; EVALUATION METRICS; HIGH SCHOOL; INTERVENTION PROGRAMS; RISK PREDICTIONS;

LEARNING ALGORITHMS;

EID: 84954107846     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2783258.2788620     Document Type: Conference Paper

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