Feature extraction and Classification of Learners using MultiContext Recurrent Neural Networks This study presents about a new procedure for extraction and classification of learners in a class using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner to improve learning process efficiency. For this purpose, this study develops a procedure to predict the achievement level of learners at the end of the class and classify them. A MultiContext Recurrent Neural Network (MCRNN) is used for predicting achievement level and classifying learners. By providing additional education for the learners who are classified as a low degree by the proposed method, it is expected to be able to take countermeasures for not becoming dropout in early stage. In this study, numerical experiments are executed to verify the usefulness of the proposed method. To gather enough number of learnersu0027 data, this study generates the learnersu0027 growth process data that used as training and test data of MCRNN. The experimental result indicates that the proposed method succeeded in classifying learners into three groups based on the understanding degree at the end of a class.

MUSERNN: A Multilayer SelfEvolving Recurrent Neural Network for Data Stream Classification In this paper, we propose MUSERNN, a multilayer selfevolving recurrent neural network model for realtime classification of streaming data. Unlike the existing approaches, MUSERNN offers special treatment towards capturing temporal aspects of data stream through its novel recurrent learning approach based on the teacher forcing policy. Novelties here are twofold. First, in contrast to the traditional RNN models, MUSERNN has intrinsic ability to selfadjust its capacity by growing and pruning hidden nodes as well as layers, to handle the everchanging characteristics of data stream. Second, MUSERNN adopts a unique scoringbased layer adaptation mechanism, which makes it capable of recalling prior tasks, with minimum exploitation of network parameters. The performance of MUSERNN is evaluated in comparison with a number of stateoftheart techniques, using seven popular data streams and continual learning problems under prequential testthentrain protocol. Experimental results demonstrate the effectiveness of MUSERNN in stream classification scenario.