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

2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease

The recommendations listed in this document are, whenever possible, evidence based. An extensive evidence review was conducted as the document was compiled through December 2008. Repeated literature searches were performed by the guideline development staff and writing committee members as new issues were considered. New clinical trials published in peer-reviewed journals and articles through December 2011 were also reviewed and incorporated when relevant. Furthermore, because of the extended development time period for this guideline, peer review comments indicated that the sections focused on imaging technologies required additional updating, which occurred during 2011. Therefore, the evidence review for the imaging sections includes published literature through December 2011