C4.5: programs for machine learning
C4.5: programs for machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
GI '05 Proceedings of Graphics Interface 2005
Using log files to track students' model-based inquiry
ICLS '06 Proceedings of the 7th international conference on Learning sciences
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
About the relationship between ROC curves and Cohen's kappa
Engineering Applications of Artificial Intelligence
Helping students make controlled experiments more informative
ICLS '10 Proceedings of the 9th International Conference of the Learning Sciences - Volume 1
Discovering and recognizing student interaction patterns in exploratory learning environments
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
User Modeling and User-Adapted Interaction
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to increase construct validity. We then compare the existing and new models on their predictive capabilities within a held-out test set in two ways. First, we analyze the models' overall predictive performance. Second, we determine how much student interaction data is necessary to make accurate predictions. We find that these reduced models with higher construct validity not only achieve better agreement overall, but also achieve better prediction with less data. This work is conducted in the context of developing models to assess students' inquiry skill at designing controlled experiments and testing stated hypotheses within a science inquiry microworld.