Learning from Learning Curves: Discovery of Interpretable Learning Trajectory Groups
Conference Paper, Proceedings of 7th International Conference on Learning Analytics and Knowledge (LAK '17), pp. 544 - 545, March, 2017
Abstract
We propose a data driven method for decomposing population level learning curve models into mutually exclusive distinctive groups each consisting of similar learning trajectories. We validate this method on six knowledge components from the log data from an online tutoring system ASSIST-ment. Preliminary analysis reveals interpretable patterns of "skill growth" that correlate with students' performance in the subsequently administered state standardized tests.
BibTeX
@conference{Chen-2017-121830,author = {Lujie Chen and Artur Dubrawski},
title = {Learning from Learning Curves: Discovery of Interpretable Learning Trajectory Groups},
booktitle = {Proceedings of 7th International Conference on Learning Analytics and Knowledge (LAK '17)},
year = {2017},
month = {March},
pages = {544 - 545},
}
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