When less is more: focused pruning of knowledge bases to improve recognition of student conversation

Authors:
Mark Floryan;Toby Dragon;Beverly Park Woolf
Affiliations:
Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA
Venue:
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Year:
2012

Citing 9
Cited 0

Answering learners' questions by retrieving question paraphrases from social Q&A sites

EANL '08 Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications
Intelligent Coaching for Collaboration in Ill-Defined Domains

Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Concepts, Structures, and Goals: Redefining Ill-Definedness

International Journal of Artificial Intelligence in Education
Expanding the Space of Plausible Solutions in a Medical Tutoring System for Problem-Based Learning

International Journal of Artificial Intelligence in Education
Persuasive dialogues in an intelligent tutoring system for medical diagnosis

ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Recognizing dialogue content in student collaborative conversation

ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
A novel pruning approach using expert knowledge for data-specific pruning

Engineering with Computers
Exploiting predictable response training to improve automatic recognition of children's spoken responses

ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Coaching within a domain independent inquiry environment

ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Expert knowledge bases are effective tools for providing a domain model from which intelligent, individualized support can be offered. This is even true for noisy data such as that gathered from activities involving ill-defined domains and collaboration. We attempt to automatically detect the subject of free-text collaborative input by matching students' messages to an expert knowledge base. In particular, we describe experiments that analyze the effect of pruning a knowledge base to the nodes most relevant to current students' tasks on the algorithm's ability to identify the content of student chat. We discover a tradeoff. By constraining a knowledge base to its most relevant nodes, the algorithm detects student chat topics with more confidence, at the expense of overall accuracy. We suggest this trade-off be manipulated to best fit the intended use of the matching scheme in an intelligent tutor.