A. Keurulainen – Deep neural networks applications for education

Antti Keurulainen (Aalto)

Thursday 2017-09-28 14.15 – 15.00

Seminar room 1021-1022, TUAS building

Deep neural networks applications for education

Antti Keurulainen has been a visiting researcher at Aalto since 2015 with a focus on deep neural networks and their applications for education and human learning. Keurulainen completed his licentiate studies and graduated in the spring of 2017. The thesis topic was “Applications of deep neural networks for assisting human learning”.

Educational applications offer opportunities to make use of advanced deep neural network methods. In this talk, Keurulainen will present three different applications and the technology behind them. A relatively simple multilayer perceptron network is constructed and trained to assess the sentiment of student feedback texts collected from Aalto students. A more complicated memory augmented network is presented to assess essays. The third example is using sequence modeling and recurrent neural networks to estimate the skills of a learner based on the previous interactions between the student and learning material. The main emphasis is on the augmenting deep neural network with an external memory component, which is currently a hot topic in machine learning research community.