Temporal Data Mining for Educational Applications
نویسندگان
چکیده
Intelligent tutoring systems (ITS) acquire rich data about students’ behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. We describe applications of data mining to challenges related to finding patterns in student actions at the level of single problems to predictions across sessions, drawing on data from two ITS for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.
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