There is an increase in the use of online practice and learning environments in education. Many of these systems adapt both the content and the level of the practice material to the individual learner. Clearly, we expect the learners to develop, but in addition we also expect the properties of the system itself to change over time due to practice, feedback, education, etc. Since reporting and feedback mechanisms in these environments depend on properties of the system as a whole, such as the abilities of learners and the difficulties of items, we expect dynamically changing model parameters and intricate non-linear dynamics in the statistical modeling of data from practice and learning environments. Nonetheless, commonly used models in educational measurement assume parameters that do not change over time. In this thesis, new methods to deal with such dynamics are discussed, with a special focus on tracking progress, i.e., following development instead of modeling it. The trackers that are introduced are suitable for all sorts of educational measurement applications where learning, practice and feedback take place.