Furthermore, experts categorize seemingly very different problems on the basis of their "deep" conceptual features, rather than on the surface features that typically capture novices' attention (Larkin et al. 1980; Chi et al. 1981; Kozma and Russell 1997).
The experts then categorized the rescreened citations as "clearly irrelevant", "unclear," or "clearly relevant" (scoring them as 0, 1, or 2, respectively).
Following a standard procedure (e.g., see word bank in [30]), the parts of selected articles that spoke to the relationship under study were examined by a field expert and categorized using a fuzzy linguistic term from the set {Very Weak (VW), Weak (W), Medium (M), Strong (S), Very Strong (VS)}.
Based on our concept mapping exercise, the expert panel categorized the 30 identified clinics into three strata of intensity; 8 clinics were assigned to the low intensity category, with 12 in the medium intensity category and 10 in the high intensity group.
Because they know more, experts can categorize not only more accurately, but also more appropriately.
The students we tested seemed to rely on surface features of chromosomal representations in much the way described by Chi and colleagues (1981), when strategies used by undergraduate physics students (novices) and advanced physics graduate students (experts) to categorize various physics problems were investigated.
A subset of the expert community can be categorized as expert dreamers.
To examine the results of CLASS-Bio beyond the overall percent agreement with the experts' score, we categorized the statements into different aspects of student thinking.
Problems raised by the experts can be categorized as: (1) on PPS performance status (2) PPS % level scores (3) PPS learning curve and (4) PPS as communication tool.
