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CS539: Project 3

CS539: Project 3. Zach Pardos. Math question response data from 592 students. 1,143 math question attributes {correct, incorrect} Average of 200 questions answered per student (lots of missing values). Class: MCAS SCORE {0-29}. Assistments Online Dataset. Assistments Online Dataset.

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CS539: Project 3

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  1. CS539: Project 3 Zach Pardos

  2. Math question response data from 592 students. 1,143 math question attributes {correct, incorrect} Average of 200 questions answered per student (lots of missing values) Class: MCAS SCORE {0-29} Assistments Online Dataset

  3. Assistments Online Dataset • Skill models: 1, 5, 39, 106

  4. Assistments Online Dataset • How well can ANNs fit the dataset with only 1, 5, 39 or 106 hidden nodes? • Default Weka values used for ANN training • Epochs: 500 • Learning: 0.3 • Momentum: 0.2 • No validation set • Training-set for testing

  5. Assistments Online Dataset • Results for training-set testing: • With 1 Hidden Node: • Correctly Classified Instances 77 • Incorrectly Classified Instances 515 • Relative absolute error 95.5309 % • With 5 Hidden Nodes: • Correctly Classified Instances 220 • Incorrectly Classified Instances 372 • Relative absolute error 77.8246 %

  6. Assistments Online Dataset • Results for training-set testing: • With 39 Hidden Nodes: • Correctly Classified Instances 590 • Incorrectly Classified Instances 2 • Relative absolute error 3.2983 % • With 106 Hidden Nodes: • Correctly Classified Instances 587 • Incorrectly Classified Instances 5 • Relative absolute error 2.8975 %

  7. Assistment Online Dataset • Conclusion: 39 and 106 models predict very well. • How well can ANNs generalize and predict instances they haven’t trained on? • Next up: 10-fold cross validation

  8. Assistment Online Dataset

  9. Assistment Online Dataset • Conclusions: • ANNs very good at fitting data • Not as good at predicting unseen cases • Possible that more nodes are required to properly generalize (more CPU!)

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