Casting Curves Before Regressions: Limitations in Refining Domain Models with Learning Curves
Carnegie Mellon University Software Engineering Institute Pittsburgh United States
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Data from student learning provide learning curves that, ideally, demonstrate improvement in student performance over time. Existing data mining methods can leverage these data to characterize and improve the domain models that support a learning environment, and these methods have been validated both with already-collected data, and in close-the-loop studies that actually modify instruction. However, these methods may be less general than previously thought, because they have not been evaluated under a wide range of data conditions.
- Numerical Mathematics