  Due to the success of deep learning (DL) and its growing job market, students
and researchers from many areas are interested in learning about DL
technologies. Visualization has proven to be of great help during this learning
process. While most current educational visualizations are targeted towards one
specific architecture or use case, recurrent neural networks (RNNs), which are
capable of processing sequential data, are not covered yet. This is despite the
fact that tasks on sequential data, such as text and function analysis, are at
the forefront of DL research. Therefore, we propose exploRNN, the first
interactively explorable educational visualization for RNNs. On the basis of
making learning easier and more fun, we define educational objectives targeted
towards understanding RNNs. We use these objectives to form guidelines for the
visual design process. By means of exploRNN, which is accessible online, we
provide an overview of the training process of RNNs at a coarse level, while
also allowing a detailed inspection of the data flow within LSTM cells. In an
empirical study, we assessed 37 subjects in a between-subjects design to
investigate the learning outcomes and cognitive load of exploRNN compared to a
classic text-based learning environment. While learners in the text group are
ahead in superficial knowledge acquisition, exploRNN is particularly helpful
for deeper understanding of the learning content. In addition, the complex
content in exploRNN is perceived as significantly easier and causes less
extraneous load than in the text group. The study shows that for difficult
learning material such as recurrent networks, where deep understanding is
important, interactive visualizations such as exploRNN can be helpful.
