Along with the advent of MOOCs and other online learning platforms such as Khan Academy, the role of online education has continued to grow in relation to that of traditional on-campus instruction. Rather than tackle the problem of evaluating large educational units such as entire online courses, this paper approaches a smaller problem: exploring a framework for evaluating more granular educational units, in this case, short educational videos. We have chosen to leverage an adaptation of traditional Bayesian Knowledge Tracing (BKT), intended to incorporate the usage of video content in addition to assessment activity. By exploring the change in predictive error when alternately including or omitting video activity, we suggest a metric for determining the relevance of videos to associated assessments. To validate our hypothesis and demonstrate the application of our proposed methods we use data obtained from both the popular Khan Academy website and two MOOCs offered by Stanford University in the summer of 2014. [For complete proceedings, see ED560503.]