Evaluation of data aging: A technique for discounting old data during student modeling

Geoffrey I. Webb, Mark Kuzmycz

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


Student modeling systems must operate in an environment in which a student’s mastery of a subject matter is likely to change as a lesson progresses. A student model is formed from evaluation of evidence about the student’s mastery of the domain. However, given that such mastery will change, older evidence is likely to be less valuable than recent evidence. Data aging addresses this issue by discounting the value of older evidence. This paper provides experimental evaluation of the effects of data aging. While it is demonstrated that data aging can result in statistically significant increases in both the number and accuracy of predictions that a modeling system makes, it is also demonstrated that the reverse can be true. Further, the effects experienced are of only small magnitude. It is argued that these results demonstrate some potential for data aging as a general strategy, but do not warrant employing data aging in its current form.

Original languageEnglish
Title of host publicationIntelligent Tutoring Systems - 4th International Conference, ITS 1998, Proceedings
EditorsCarol L. Redfield, Barry P. Goettl, Valerie J. Shute, Henry M. Halff
Number of pages10
ISBN (Print)3540647708, 9783540647706
Publication statusPublished - 1 Jan 1998
Externally publishedYes
Event4th International Conference on Intelligent Tutoring Systems, ITS 1998 - San Antonio, United States of America
Duration: 16 Aug 199819 Aug 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th International Conference on Intelligent Tutoring Systems, ITS 1998
Country/TerritoryUnited States of America
CitySan Antonio

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