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

International Journal of Learning Technology, 2014 Vol.9 No.3, pp.281 - 303

Published online: 27 Nov 2014 *

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