Computer based training has become an increasingly attractive alternative to traditional training methods for skill acquisition, and the topic of skill modeling has become one of great interest. One of the key problems in computer-based training is automatic skill evaluation, which requires precise and accurate skill modeling. In order to evaluate and model human skills it is necessary to identify a relevant set of attributes by which skill can be measured, interpreted and evaluated by computers. In this paper we present our work on attributed skill transfer and focus on the specific task of writing. Identifying the central attributes associated with writing and analyzing the consistencies of these attributes within and among subjects determines whether one unique expert model can be derived from a pool of experts. In our study key attributes of hand writing were identified and statistical analysis on subject data was conducted. In our analysis we found experts' behavior can be modeled using the parameters and the same could be used to distinguish between experts and novices, lending itself as an evaluation tool.