Abstract. Rewrite strategies are used to specify how mathematical exercises are solved in interactive learning environments, and to provide feedback to students solving such exercises. We have developed a generic strategy language with which we can specify rewrite strategies in many (mathematical) domains. Although our strategy language is quite powerful, it lacks an essential component for specifying strategies, namely the interleaving of two strategies. Often students have to perform multiple subtasks, but the order in which these tasks are performed is irrelevant, and steps of solutions may be interleaved. We show the need for combinators that support interleaving by means of several examples. We extend our strategy language with different combinators for interleaving, define the semantics of the extension, and show how the interleaving combinators are implemented in the parsing framework we use for recognizing student behavior and providing hints.

Mathematical learning environments give domain-specific and immediate feedback to students solving a mathematical exercise. Based on a language for specifying strategies, we have developed a feedback framework that automatically calculates semantically rich feedback. We offer this feedback functionality to mathematical learning environments via a set of web services. Feedback is only effective when it is precise and to the point. The tests we have performed give some confidence about the correctness of our feedback services. To increase confidence in our services, we explicitly specify the properties our feedback services should satisfy, and, if possible, prove them correct. For this, we give a formal description of the concepts used in our feedback framework services. The formalisation allows us to reason about these concepts, and to state a number of desired properties of the concepts. Our feedback services use exercise descriptions for their instances on domains such as logic, algebra, and linear algebra. We formulate requirements these domain descriptions should satisfy for the feedback services to react as expected. 1

Math-Bridge is a European project which aims to provide facilities for bridging the mathematics gap between schools and higher education in Europe. The Open Universiteit Nederland is responsible for the interactive exercise player for Math-Bridge. This paper discusses the various forms interactions can take when solving mathematical exercises, the kind of feedback an exercise player should give according to teachers and developers of learning environments, and how strategies can be used to automatically calculate many of these kinds of feedback. Furthermore, it discusses some of the peculiarities of mathematical exercises that challenge our strategy framework. 1

Abstract. Mathematical learning environments help students in mastering mathematical knowledge. Mature environments typically offer thousands of interactive exercises. Providing feedback to students solving interactive exercises requires domain reasoners for doing the exercisespecific calculations. Since a domain reasoner has to solve an exercise in the same way a student should solve it, the structure of domain reasoners should follow the layered structure of the mathematical domains. Furthermore, learners, teachers, and environment builders have different requirements for adapting domain reasoners, such as providing more details, disallowing or enforcing certain solutions, and combining multiple mathematical domains in a new domain. In previous work we have shown how domain reasoners for solving interactive exercises can be expressed in terms of rewrite strategies, rewrite rules, and views. This paper shows how users can adapt and configure such domain reasoners to their own needs. This is achieved by enabling users to explicitly communicate the components that are used for solving an exercise. 1

and a competence model for rule based e-tutoring systems

We introduce an interactive tutor that supports the stepwise development of simple functional programs. Using this tutor, students receive feedback about whether or not they are on the right track, can ask for a hint when they are stuck, and get suggestions about how to refactor their program. Our tutor generates this semantically rich feedback from model solutions, using advanced concepts from software technology. We show how a teacher can add an exercise to the tutor, and fine-tune feedback. We report on an experiment in which we used our tutor.