Faculty Advisor

Beck, Joseph E.

Center

WORC / Worcester Community Project Center

Abstract

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%.

Publisher

Worcester Polytechnic Institute

Date Accepted

January 2018

Major

Computer Science

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

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