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


Shubhendu Trivedi, Zachary Pardos and Neil Heffernan

Paper type: 
Full paper
14. 10:30-12:30, Friday 1 July


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