The 2007 international timetabling competition (ITC) consisted of three competition tracks; one examination timetabling and two course timetabling. Thomas Muller’s hyper-heuristic framework CPSolver obtained first place in two out of these three tracks. This project presents the effects of modifying the CPSolver framework, by implementing a sequence-based heuristic selection technique to operate as a Hidden Markov Model within the CPSolver framework. We aim to improve upon the overall solution value for each data instance used in the competition. This technique makes use of a matrix which maintains transitional probabilities between each low-level heuristic to select the next heuristic in the sequence. A sec- ond matrix tracks the probabilities of ending the sequence on a given low-level heuristic. Each component of the proposed technique is explored in depth to help tailor the heuristic selection process for the given data instance, and uncover Hidden Markov chains. When applied to the 2007 ITC data instances, the number of soft constraint violations were on average reduced, producing improved solutions.