The purpose of this work is to assess divergent thinking using a new coding matrix composed of categories derived from neuropsychology and cognitive science theory. Using the matrix, interview data were coded and characterized for divergent production using ten coefficients: higher category responses, detail, spontaneous generation, perspective switching, category switches/probe and per utterance, different switches, detail/switching, new subjects, and reasoning.
Results indicated the coding scheme is sensitive to detect individual differences, providing a way of assessing divergent thinking that may be related to creative thinking in science learning.
A test was also used to assess how many different scientific examples the respondent could identify. A statistically significant correlation was found between some coefficients and the number of diverse examples verbalized. Factor analysis identified five factors including a major factor encompassing divergent thinking separate from one identified as cognitive ability. These results suggest that the model may be a promising way of further investigating neurocognitive bases for divergent thinking in science.