The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Notes

Associated Lab(s) / Group(s): Project LISTEN

Associated Project(s): Project LISTEN's Reading Tutor

Number of pages: 9

Text Reference

Jack Mostow, "Some useful design tactics for mining ITS data," Proceedings of the ITS2004 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes, August, 2004, pp. 20 - 28.

BibTeX Reference

@inproceedings{Mostow_2004_4994,
   author = "Jack Mostow",
   title = "Some useful design tactics for mining ITS data",
   booktitle = "Proceedings of the ITS2004 Workshop on Analyzing Student-Tutor Interaction Logs to Improve Educational Outcomes",
   pages = "20 - 28",
   month = "August",
   year = "2004",
}

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