Applying Automated Originality Scoring to the Verbal Form of Torrance Tests of Creative Thinking

Abstract

In this study, we applied different text-mining methods to the originality scoring of the Unusual Uses Test (UUT) and Just Suppose Test (JST) from the Torrance Tests of Creative Thinking (TTCT)–Verbal. Responses from 102 and 123 participants who completed Form A and Form B, respectively, were scored using three different text-mining methods. The validity of these scoring methods was tested against TTCT’s manual-based scoring and a subjective snapshot scoring method. Results indicated that text-mining systems are applicable to both UUT and JST items across both forms and students’ performance on those items can predict total originality and creativity scores across all six tasks in the TTCT-Verbal. Comparatively, the text-mining methods worked better for UUT than JST. Of the three text-mining models we tested, the Global Vectors for Word Representation (GLoVe) model produced the most reliable and valid scores. These findings indicate that creativity assessment can be done quickly and at a lower cost using text-mining approaches.