At schools special learning and programming environments are often used in the field of algorithms. Particularly with regard to computer science lessons in secondary education, they are supposed to help novices to learn the basics of programming. In several parts of Germany (e.g., Bavaria) these fundamentals are taught as early as in the seventh grade, when pupils are 12 to 13 years old. Designed age-based learning and programming environments such as Karel the robot and Kara, the programmable ladybug, are used, but learners still underachieve. One possible approach to improving both the teaching and the learning process is to specify the knowledge concerning the learners' individual problem solving strategies, their solutions, and their respective quality. A goal of the research project described here is to design the learning environment so that it can identify and categorize several problem-solving strategies automatically. Based on this knowledge, learning and programming environments can be improved, which will optimize the computer science lessons in which they are applied. Therefore, the environments must be enhanced with special analytic and diagnostic modules, the results of which can be given to the learner in the form of individualized system feedback messages in the future. In this text preliminary considerations are demonstrated. The research methodology as well as the design and the implementation of the research instruments are explained. We describe first studies, whose results are presented and discussed.