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.
- {Koedinger et al. 2010} Koedinger, K. R., R. S. J. Baker, K. Cunningham, and A. Skogsholm (2010). A Data Repository for the EDM community : The PSLC DataShop. Handbook of Educational Data Mining, 43--55.Google Scholar
- {Nagin 2005} Nagin, D. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.Google Scholar
- {Trivedi et al. 2011} Trivedi, S., Z. Pardoz, and N. Heffernan (2011). Spectural Clustering in Educational Data MIning. Proceedings of the 4th International Conference on Educational Data Mining, 129--138.Google Scholar
Index Terms
- Learning from learning curves: discovering interpretable learning trajectories
Recommendations
How Bad May Learning Curves Be?
In this paper, we motivate the need for estimating bounds on learning curves of average-case learning algorithms when they perform the worst on training samples. We then apply the method of reducing learning problems to hypothesis testing ones to ...
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks
Machine Learning and Knowledge Discovery in DatabasesAbstractThe use of learning curves for decision making in supervised machine learning is standard practice, yet understanding of their behavior is rather limited. To facilitate a deepening of our knowledge, we introduce the Learning Curve Database (LCDB), ...
Comments