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Survival Analysis on Duration Data in Intelligent Tutors

  • Michael Eagle
  • Tiffany Barnes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

Effects such as student dropout and the non-normal distribution of duration data confound the exploration of tutor efficiency, time-in-tutor vs. tutor performance, in intelligent tutors. We use an accelerated failure time (AFT) model to analyze the effects of using automatically generated hints in Deep Thought, a propositional logic tutor. AFT is a branch of survival analysis, a statistical technique designed for measuring time-to-event data and account for participant attrition. We found that students provided with automatically generated hints were able to complete the tutor in about half the time taken by students who were not provided hints. We compare the results of survival analysis with a standard between-groups mean comparison and show how failing to take student dropout into account could lead to incorrect conclusions. We demonstrate that survival analysis is applicable to duration data collected from intelligent tutors and is particularly useful when a study experiences participant attrition.

Keywords

ITS EDM Survival Analysis Efficiency Duration Data 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael Eagle
    • 1
  • Tiffany Barnes
    • 1
  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA

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