Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions

  • Shubhendu Trivedi
  • Zachary A. Pardos
  • Neil T. Heffernan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)


In typical assessment student are not given feedback, as it is harder to predict student knowledge if it is changing during testing. Intelligent Tutoring systems, that offer assistance while the student is participating, offer a clear benefit of assisting students, but how well can they assess students? What is the trade off in terms of assessment accuracy if we allow student to be assisted on an exam. In a prior study, we showed the assistance with assessments quality to be equal. In this work, we introduce a more sophisticated method by which we can ensemble together multiple models based upon clustering students. We show that in fact, the assessment quality as determined by the assistance data is a better estimator of student knowledge. The implications of this study suggest that by using computer tutors for assessment, we can save much instructional time that is currently used for just assessment.


Clustering Ensemble Learning Intelligent Tutoring Systems Regression Dynamic Assessment Educational Data Mining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shubhendu Trivedi
    • 1
  • Zachary A. Pardos
    • 1
  • Neil T. Heffernan
    • 1
  1. 1.Department of Computer ScienceWorcester Polytechnic InstituteWorcesterUnited States

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