Feature Extraction and Classification of Learners Using Neural Networks

This paper to Practice Full Paper presents about a procedure to generate the learners' data using a learner model based on first-order lag system to generate learners data. By using the generated data, the learners are classified into several groups and sonic learners with low understanding degree can be extracted by using the neural networks. It is necessary to provide learning support corresponding to the understanding degree of each learner in a class to improve effective learning. By providing additional education for the learners who are predicted as low degree by the proposed procedure, it is expected to take countermeasures for not becoming "dropout students" in early stage. For this purpose, predicting the understanding degree is important, and this paper employs a recurrent neural network as the predictor. Hayashida et at. (2018) have constructed to classify the learners by understanding degree at the end of the class based on some observed data such as result of quizzes, or report tasks by using FNN (Feedforward Neural Network). This paper uses a RNN (Recurrent Neural Network) which consists of feedforward signal processing and structure of the signal feedbacks because of the observed data is time-series data. This paper proposes a learner model based on first-order lag system to generate learner data for training RNNs. As experimental result of the simulation, this paper succeeds in extracting the learner group with low understanding degree in future.