A cognitive task analysis for dental hygiene.

To be an effective assessment tool, a simulation-based examination must be able to evoke and interpret observable evidence about targeted knowledge, strategies, and skills in a manner that is logical and defensible. Dental Interactive Simulations Corporation's first assessment effort is the development of a scoring algorithm for a simulation-based dental hygiene initial licensure examination. The first phase in developing a scoring system is the completion of a cognitive task analysis (CTA) of the dental hygiene domain. In the first step of the CTA, a specifications map was generated to provide a framework of the tasks and knowledge that are important to the practice of dental hygiene. Using this framework, broad classes of behaviors that would tend to distinguish along the dental hygiene expert-novice continuum were identified. Nine paper-based cases were then designed with the expectation that the solutions of expert, competent, and novice dental hygienists would differ. Interviews were conducted with thirty-one dental hygiene students/practitioners to capture solutions to the paper-based cases. Transcripts of the interviews were analyzed to identify performance features that distinguish among the interviewees on the basis of their expertise. These features were more detailed and empirically grounded than the originating broad classes and better serve to ground the design of a scoring system. The resulting performance features were collapsed into nine major categories: 1) gathering and using information, 2) formulating problems and investigating hypotheses, 3) communication and language, 4) scripting behavior, 5) ethics, 6) patient assessment, 7) treatment planning, 8) treatment, and 9) evaluation. The results of the CTA provide critical information for defining the necessary elements of a simulation-based dental hygiene examination.

Resolving mixtures of strategies in spatial visualization tasks.

The models of standard test theory, having evolved under a trait-oriented psychology, do not reflect the knowledge structures and the problem-solving strategies now seen as central to understanding performance and learning. In some applications, however, key qualitative distinctions among persons as to structures and strategies can be expressed through mixtures of test theory models, drawing on substantive theory to delineate the components of the mixture. This approach is illustrated with response latencies to spatial visualization tasks that can be solved by mental rotation or by a non-rotational rule-based strategy. It is assumed that a subject employs the same strategy on all tasks, but the possibility of extending the approach to strategy-switching is discussed.