Understanding of algorithms is one of the most challenging aspects of the study of Computer Science. Over two decades of research has been devoted to improving algorithm visualization and Algorithm Explanation techniques. The Structured Hypermedia Algorithm Explanation system (SHALEX) is our implementation of an algorithm explanation environment. SHALEX facilitates understanding of algorithms using a tree-oriented representation of the algorithm. To evaluate student performance, each algorithm explanation has an author model of an "ideal" student. However, there is currently no facility in SHALEX for using these data or for providing individually tailored help to students. Our paper presents an Algorithm Explanation Teaching Agent (AETA) which aids in student understanding of Algorithm Explanations in SHALEX. To evaluate the student progress, the AETA monitors the completion rate and compares it against the author model. As the student studies an Algorithm Explanation the AETA dynamically changes its interactions.