A Neural Network Approach to Automated Classification of Elementary Mathematics Instructional Activities

摘要

Automated analysis of classroom video for teaching evaluation has eluded current solutions. A few types of neural networks have recently proven effective for video classification: convolutional neural networks (CNNs), long short-term memory (LSTM) neural networks, and hybrid CNN-LSTMs. We use classification accuracy to evaluate the efficacy of each approach while taking account of practical considerations such as the computational burden of each method as well as the training set required.