Enhancing the Automatic Generation of Hints with Expert Seeding

Issue title: Best of ITS 2010

Article type: Research Article

Authors: Stamper, John | Barnes, Tiffany | Croy, Marvin

Affiliations: Human-Computer Interaction Institute, Carnegie Mellon University, john@stamper.org | Department of Computer Science, University of North Carolina at Charlotte, tiffany.barnes@gmail.com | Department of Philosophy, University of North Carolina at Charlotte, mjcroy@uncc.edu

Abstract: The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to “seed” the hint generation process. We show that just a few expert solutions give significant coverage (over 50%) for hints. This seeding method greatly speeds up the time needed to reliably generate hints. We discuss how this feature can be integrated into the Hint Factory and some potential pedagogical issues that the expert solutions introduce.

Keywords: Educational data mining, markov decision process, hints

DOI: 10.3233/JAI-2011-021

Journal: International Journal of Artificial Intelligence in Education, vol. 21, no. 1-2, pp. 153-167, 2011