Diagnosing Errors in Statistical Problem-Solving: Associative Problem Recognition and Plan-Based Error Detection

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

Evaluating a Web-Based Interface for Internet Telemedicine

The objective is to introduce the usability engineering methodology, heuristic evaluation, to the design and development of a web-based telemedicine system. Using a set of usability criteria, or heuristics, one evaluator examined the Spacebridge to Russia web-site for usability problems. Thirty-four usability problems were found in this preliminary study and all were assigned a severity rating. The value of heuristic analysis in the iterative design of a system is shown because the problems can be fixed before deployment of a system and the problems are of a different nature than those found by actual users of the system. It was therefore determined that there is potential value of heuristic evaluation paired with user testing as a strategy for optimal system performance design

Using Mental Schemata: An expeimental Analysis of Computer Skill Acquisition

Although mental schemata are central to many contemporary learning theories, theprecise relationship between such schemata and specific types of learning remains vague.This paper describes an analysis of subjects learning basic computer skills when presentedwith four different kinds of elaborations that should influence subjects' schemata: (I) asimple description with no model: (2) a redundant elaborated text also with no explicitmodel: (3) a functional model: (4) a descriptive analogy. Subjects were tested onprocedures, general comman d concepts, and system questions. Models and analogies wereshown to improve initial performance on all types of questions. However only systemquestions showed this advantage after a delay. It is argued that the utility of building amental model through elaboration depends on the specific tasks that are analyzed