  The increasing popularity of e-learning has created demand for improving
online education through techniques such as predictive analytics and content
recommendations. In this paper, we study learner outcome predictions, i.e.,
predictions of how they will perform at the end of a course. We propose a novel
Two Branch Decision Network for performance prediction that incorporates two
important factors: how learners progress through the course and how the content
progresses through the course. We combine clickstream features which log every
action the learner takes while learning, and textual features which are
generated through pre-trained GloVe word embeddings. To assess the performance
of our proposed network, we collect data from a short online course designed
for corporate training and evaluate both neural network and non-neural network
based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and
0.958 AUC score, which outperforms all other models. The results also indicate
the combination of behavior features and text features are more predictive than
behavior features only and neural network models are powerful in capturing the
joint relationship between user behavior and course content.

Machine Learning;Human-Computer Interaction