MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we can predict a student's next item response with over 88% accuracy. Using these predictions, targeted intervention scan be offered to students and targeted improvements can be made to courses. In particular, this approach would allow for gating of content until a student has reasonable likelihood of succeeding. [This paper was published in: "The 9th International Learning Analytics Knowledge Conference (LAK19), March 4-8, 2019, Tempe, AZ, USA" (pp. 340-342). New York, NY: ACM, 2019. (ISBN 978-1-4503-6256-6)]