Int. J. of Learning Technology   »   2014 Vol.9, No.3

 

 

Title: Predicting semantic changes in abstraction in tutor responses to students

 

Authors: Michael Lipschultz; Diane Litman; Sandra Katz; Patricia Albacete; Pamela Jordan

 

Addresses:
University of Pittsburgh, 6311 Sennott Square, 210 South Bouquet Street, Pittsburgh, PA 15260, USA
741 Learning Research & Development Center University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA 15260, USA
733 Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA 15260, USA
708 Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA 15260, USA
701 Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA 15260, USA

 

Abstract: Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we consider semantic changes. Since we are interested in developing a fully-automatic computer-based tutor, we use only automatically-extractable features (e.g., percent of domain words in student turn) or features available in a tutoring system (e.g., correctness). We find patterns that predict tutor changes in abstraction better than a majority class baseline. Generalisation is best-predicted using student and reflection features. Specification is best-predicted using student and problem features.

 

Keywords: intelligent tutoring systems; ITS; natural language processing; NLP; abstraction changes; reflective tutorial dialogues; semantic changes; learning technologies; tutor responses; student feedback.

 

DOI: 10.1504/IJLT.2014.065753

 

Int. J. of Learning Technology, 2014 Vol.9, No.3, pp.281 - 303

 

Available online: 14 Nov 2014

 

 

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