This paper describes our model for diagnosis of student errors in statistical problem solving.A simulation of that diagnosis, GIDE, is presented together with empirical validation on student solutions. The model consists of two components. An "intention based"diagnostic component analyzes solutions and locates errors by trying to synthesize student solutions from knowledge about the goal structure of the problem and related knowledge about planning errors. This approach can account for about 8 2 % of the lines and over 9 5 % of the goals in a set of 60 student t-tests. When solutions contain errors in procedural implementation such plan-based analysis is quite effective. In many cases,however, students do not pursue an "appropriate" solution path. The diagnostic model,therefore, includes a second component which is used to determine which type of problem the student is using, it is modeled by a spreading activation network of statistical knowledge.On a sample of 38 student solutions, the simulation correctly identified 8 6 % of the problem types. The model appears to account for a wide range of problem-solving behavior within the domain studied. The preliminary performance data suggest that our model may serve as a useful part of an intelligent tutoring system.
