TY - JOUR
T1 - Identifying Creativity During Problem Solving Using Linguistic Features
AU - Skalicky, Stephen
AU - Crossley, Scott A.
AU - McNamara, Danielle
AU - Muldner, Kasia
N1 - Funding Information:
Acknowledgments: We thank Win Burleson for his role as PI on DRL-1319645. This work was supported by the Institute of Education Sciences [IESR305A080589, R305G20018-02] and National Science Foundation [DRL-1319645].
Publisher Copyright:
Copyright © Taylor & Francis Group, LLC.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants’ language while completing collaborative divergent thinking tasks. To this end, 78 participants’ conversational dialogs (i.e., 39 dyads) within a chat environment were collected while completing three open-ended problem-solving tasks. Expert raters scored the dialogs in terms of fluency, flexibility, elaboration, and originality, as well as three types of creative language (metaphor and simile, humor, and word play). Factor analyses indicated that these scores captured two main constructs (creativity and elaboration). The linguistic features of the participants’ language (captured computationally using natural language processing tools) accounted for significant amounts of variation in both the creativity (R2 =.640) and elaboration (R2 =.550) scores within linear mixed effect (LME) models. These results highlight specific linguistic features that can be used to explain large amounts of variance in constructs related to creativity.
AB - Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants’ language while completing collaborative divergent thinking tasks. To this end, 78 participants’ conversational dialogs (i.e., 39 dyads) within a chat environment were collected while completing three open-ended problem-solving tasks. Expert raters scored the dialogs in terms of fluency, flexibility, elaboration, and originality, as well as three types of creative language (metaphor and simile, humor, and word play). Factor analyses indicated that these scores captured two main constructs (creativity and elaboration). The linguistic features of the participants’ language (captured computationally using natural language processing tools) accounted for significant amounts of variation in both the creativity (R2 =.640) and elaboration (R2 =.550) scores within linear mixed effect (LME) models. These results highlight specific linguistic features that can be used to explain large amounts of variance in constructs related to creativity.
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U2 - 10.1080/10400419.2017.1376490
DO - 10.1080/10400419.2017.1376490
M3 - Article
AN - SCOPUS:85032699696
SN - 1040-0419
VL - 29
SP - 343
EP - 353
JO - Creativity Research Journal
JF - Creativity Research Journal
IS - 4
ER -