Two-Stage Predictive Modeling for Identifying At-Risk Students

  • Brett E. Shelton
  • Juan Yang
  • Jui-Long Hung
  • Xu Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11003)


This study proposes an analytic approach which combines two predictive models (the predictive model of successful students and the predictive model of at-risk students) to enhance prediction performance for use under the constraints of limited data collection. A case study was conducted to examine the effects of the model combination approach. Eight variables were collected from a data warehouse and the Learning Management System. The best model was selected based on the lowest misclassification rate in the validation dataset. The confusion matrix compares the model’s performance with the following parameters: accuracy, misclassification, and sensitivity. The results show the new combination approach can capture more at-risk students than the singular predictive model, and is only suitable for the ensemble predictive algorithms.


Learning analytics Academic at-risk factors Academic success factors Ensemble model 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Brett E. Shelton
    • 1
  • Juan Yang
    • 2
  • Jui-Long Hung
    • 1
  • Xu Du
    • 2
  1. 1.Boise State UniversityBoiseUSA
  2. 2.National Engineering Research Center for E-Learning, Central China Normal UniversityWuhanChina

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