If I Have a Hammer:
Computational Linguistics in a Reading Tutor that Listens

Jack Mostow, Research Professor
Robotics, Language Technologies, Human-Computer Interaction, Automated Learning and Discovery
Director, Project LISTEN
Carnegie Mellon University
RI-NSH 4213, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890
www.cs.cmu.edu/~listen

Project LISTEN’s Reading Tutor uses speech recognition to listen to children read aloud, and helps them learn to read, as evidenced by rigorous evaluations of pre- to posttest gains compared to various controls. In the 2003-2004 school year, children ages 5-14 used the Reading Tutor daily at school on over 200 computers, logging over 50,000 sessions, 1.5 million tutorial responses, and 10 million words.
This talk uses the Reading Tutor to illustrate the diverse roles that computational linguistics can play in an intelligent tutor:

· A domain model describes a skill to learn, such as mapping from spelling to pronunciation.
· A production model predicts student behavior, such as likely oral reading mistakes.
· A language model predicts likely word sequences for a given task, such as oral reading.
· A student model estimates a student’s skills, such as mastery of grapheme-to-phoneme mappings.
· A pedagogical model guides tutorial decisions, such as choosing words a student is ready to try.

A recurring theme is the use of “big data” to train such models automatically.

Acknowledgements

This work was supported in part by the National Science Foundation under ITR/IERI Grant No. REC-0326153. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Science Foundation or the official policies, either expressed or implied, of the sponsors or of the United States Government.
I thank the students and educators who generated our data, and the current and past members of Project LISTEN for contributions to this work, including their publications posted at www.cs.cmu.edu/~listen.