The problem we are trying to solve is to tweak and improve several neural networks for prediction of student correctness on questions. The approach we adopt to solve the problem is to compare different features (drop rate, number of hidden layers, hidden nodes, with/without autoencoder, optimize function, batch size, fully connected or not) in the neural network so that we can improve AUC (Area under Curve) to get the better prediction results. The results obtained in this research include all the AUC data from all the different neural networks. Our obtained results are great basics of following working on the neural network and succeeding studies in the field of education. The highest accuracy of our single output network reached 87%.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Shen, Jingwei

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-012518-155734

Advisor

• Beck, Joseph E.

Year

• 2018

Date created

• 2018-01-25

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright