@inproceedings{8625f2dc99794551b582f6fa40f28805,
title = "Students{\textquoteright} walk through tutoring: Using a random walk analysis to profile students",
abstract = "The purpose of this study was to investigate students{\textquoteright} patterns of interactions within a game-based intelligent tutoring system (ITS), and how those interactions varied as a function of individual differences. The analysis presented in this paper comprises a subset (n=40) of a larger study that included 124 high school students. Participants in the current study completed 11 sessions within iSTART-ME, a game-based ITS, that provides training in reading comprehension strategies. A random walk analysis was used to visualize students{\textquoteright} trajectories within the system. The analyses revealed that low ability students{\textquoteright} patterns of interactions were anchored by one feature category whereas high ability students demonstrated interactions across multiple categories. The results from the current paper indicate that random walk analysis is a promising visualization tool for learning scientists interested in capturing students{\textquoteright} interactions within ITSs and other computer-based learning environments over time.",
keywords = "Individual differences, Intelligent Tutoring Systems, Random walk analysis, Sequential pattern analysis",
author = "Snow, {Erica L.} and Likens, {Aaron D.} and {Tanner Jackson}, G. and McNamara, {Danielle S.}",
year = "2013",
month = jan,
day = "1",
language = "English (US)",
series = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
publisher = "International Educational Data Mining Society",
editor = "D'Mello, {Sidney K.} and Calvo, {Rafael A.} and Andrew Olney",
booktitle = "Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013",
note = "6th International Conference on Educational Data Mining, EDM 2013 ; Conference date: 06-07-2013 Through 09-07-2013",
}