Some useful design tactics for mining ITS data
Workshop Paper, ITS '04 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, pp. 20 - 28, August, 2004
Abstract
Mining data logged by intelligent tutoring systems has the potential to reveal valuable discoveries. What characteristics make such data conducive to mining? What variables are informative to compute? Based on our experience in mining data from Project LISTEN's Reading Tutor, we discuss how to collect machine-analyzable data and formulate it into experimental trials. The resulting concepts and tactics mark out a roadmap for the emerging area of tutorial data mining, and may provide a useful vocabulary and framework for characterizing past, current, and future work in this area.
BibTeX
@workshop{Mostow-2004-9001,author = {Jack Mostow},
title = {Some useful design tactics for mining ITS data},
booktitle = {Proceedings of ITS '04 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes},
year = {2004},
month = {August},
pages = {20 - 28},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.