Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments

Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has the potential to improve educational outcomes. A promising approach for adaptation in OELEs is the design of interventions based on the detection of characteristic learning behaviors through offline clustering, followed by a real-time classification of new students. In this paper, we explore the possibility of using recurrent neural network (RNN) models for this online classification task. We extensively evaluate the predictive performance of different variants of RNNs, namely long-short term memory models and gated recurrent units, and different architectures on a data set collected from an exploration-based educational game. We also compare the prediction accuracy of the different RNN models to the performance of traditional classifiers on the same data set. Our results demonstrate that RNNs perform similar or better than traditional methods regarding early classification and therefore constitute a promising alternative for the online classification of new students. [For the full proceedings, see ED599096.]