We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a time-series prediction methodology that uses a recurrent neural network architecture to pass network state between time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on four real-world datasets from Massive Open Online Course (MOOC) discussion forums, we find that our method obtains substantial improvements over a Bayesian model and an unsupervised baseline, with AUCs typically above 0.75 and reaching 0.97 depending on the dataset. Our feature importance analysis shows that while neighborhood-based features contribute the most to the results, post-based and path-based features add additional information that significantly improve the predictions. We also find that several input features have opposite directions of correlation between link formation and post quality, suggesting that response time and quality are two competing objectives to be accounted for in SLN link recommendation systems.