Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support recommendations. By providing Bayesian Knowledge Tracing mastery estimates to a contextual multi-armed bandit as user-level context in a simulated environment, we demonstrate that using domain knowledge to engineer contextual features results in higher average cumulative reward, and significant improvement over randomly selecting student supports. The data used to simulate sequential recommendations are available at https://osf.io/sfyzv/?view_only=351fb8781d2c4f3bbc9d7486762d563a. [Additional funding provided by Hewlett. This paper was published in: "The 14th Learning Analytics and Knowledge Conference (LAK '24), March 18-22, 2024, Kyoto, Japan," ACM, 2024, pp. 864-870.]