International Conference on Artificial Intelligence in Education

AIED 2011: Artificial Intelligence in Education pp 23-30

Towards Predicting Future Transfer of Learning

  • Ryan S. J. d. Baker
  • Sujith M. Gowda
  • Albert T. Corbett
Conference paper

DOI: 10.1007/978-3-642-21869-9_6

Volume 6738 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Baker R.S.J.., Gowda S.M., Corbett A.T. (2011) Towards Predicting Future Transfer of Learning. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg

Abstract

We present an automated detector that can predict a student’s future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper post-tests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student’s data from a tutor lesson) in order to reach near-asymptotic predictive power.

Keywords

Transfer Bayesian Knowledge Tracing Educational Data Mining Student Modeling Robust Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryan S. J. d. Baker
    • 1
  • Sujith M. Gowda
    • 1
  • Albert T. Corbett
    • 2
  1. 1.Department of Social Science and Policy StudiesWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Human-Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA