Publications

• How do the kids speak? Improving educational use of text mining with child-directed language models (2023)
  • Abstract: Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. The purpose of this study is to determine whether models used by computational text mining applications need to be adapted when used with samples of elementary-aged children.
  • Citation: Organisciak, P., Acar, S., Newman, M., Dumas, D., & Eby, D. (2023). How do the kids speak? Modeling child-directed language for educational use. Information and Learning Sciences. https://doi.org/10.1108/ILS-06-2022-0082 (Available at SSRN: https://ssrn.com/abstract=4329061)
• Measuring flexibility: A text-mining approach (2023)
  • Abstract: In creativity research, ideational flexibility, the ability to generate ideas by shifting between concepts, has long been the focus of investigation. However, psychometric work to develop measurement procedures for flexibility has generally lagged behind other creativity-relevant constructs such as fluency and originality. Here, we build from extant research to theoretically posit, and then empirically validate, a text-mining based method for measuring flexibility in verbal divergent thinking (DT) responses. The empirical validation of this method is accomplished in two studies. In the first study, we use the verbal form of the Torrance Test of Creative Thinking (TTCT) to demonstrate that our novel flexibility scoring method strongly and positively correlates with traditionally used TTCT flexibility scores. In the second study, we conduct a confirmatory factor analysis using the Alternate Uses Task to show reliability and construct validity of our text-mining based flexibility scoring. In addition, we also examine the relationship between personality facets and flexibility of ideas to provide criterion validity of our scoring methodology. Given the psychometric evidence presented here and the practicality of automated scores, we recommend adopting this new method which provides a less labor-intensive and less costly objective measurement of flexibility.
  • Citation: Grajzel, K., Acar, S., Dumas, D., Organisciak, P., & Berthiaume, K. (2023). Measuring flexibility: A text-mining approach. Frontiers in Psychology, 13. https://www.frontiersin.org/articles/10.3389/fpsyg.2022.1093343
• Beyond Semantic Distance: Automated Scoring of Divergent Thinking Greatly Improves with Large Language Models (2022)
  • Abstract: Automated scoring for divergent thinking seeks to overcome a key obstacle to creativity measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test of divergent thinking, the Alternate Uses Task (AUT), the primary automated approach casts the problem as a semantic distance between a prompt and the resulting idea in a text model. This work presents an alternative approach that greatly surpasses the performance of the best existing semantic distance approaches. Our system fine-tunes deep neural network-based large-language models (LLMs) on human-judged responses. Trained and evaluated against one of the largest collections of human-judged AUT responses, with 27 thousand responses collected from nine past studies, our fine-tuned large-language-models achieved up to r = .81 correlation with human raters, greatly surpassing current systems (r = .12-.26). Further, learning transfers well to new test items and the approach is still robust with small numbers of training labels; in some cases, without any training at all. This work also suggests a limit to the underlying assumptions of the semantic distance model, showing that a purely semantic approach that uses the stronger language representation of LLMs, while still improving on existing systems, does not achieve comparable improvements to our fine-tuned system. The increase in performance can support stronger applications and interventions in divergent thinking and opens the space of automated divergent thinking scoring to new areas for improving and understanding this branch of methods.
  • Citation: Organisciak, P., Acar, S., Dumas, D., Berthiaume, K. (Pre-print, 2022). Beyond Semantic Distance: Automated Scoring of Divergent Thinking Greatly Improves with Large Language Models. http://dx.doi.org/10.13140/RG.2.2.32393.31840
• Applying automated originality scoring to the verbal form of Torrance Tests of Creative Thinking (2021)
  • 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.
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  • Citation: Acar, S., Berthiaume, K., Grajzel, K., Dumas, D., Flemister, C. T., & Organisciak, P. (2021). Applying automated originality scoring to the verbal form of Torrance Tests of Creative Thinking. Gifted Child Quarterly. Advance online publication. https://doi.org/10.1177/00169862211061874
• Measuring up: Aligning creativity assessment with the Standards. In M. Runco & S. Acar (Eds.), Handbook of Creativity Assessment. (In Press)
  • Citation: Dumas, D., & Grajzel, K.* (in press). Measuring up: Aligning creativity assessment with the Standards. In M. Runco & S. Acar (Eds.), Handbook of Creativity Assessment. Cheltenham, UK: Edward Elgar Publishing.

Presentations

• Berthiaume, K. (Chair) (2022). Innovations in methodological approaches in creativity assessments for gifted education. Symposium accepted for presentation at American Educational Research Association Annual Meeting, San Diego, CA.
  • Dumas, D., & Dong, Y. (April, 2022). Observing students’ zone of proximal creativity using a dynamic assessment procedure. In K. Berthiaume (Chair), Innovations in Methodological Approaches in Creativity Assessments for Gifted Education. Symposium to be presented at the annual meeting of the American Educational Research Association, San Diego, CA.
  • Grajzel, K., Dumas, D., Berthiaume, K., Acar, S., & Organisciak, P. (April, 2022) Measuring flexibility: A text-mining approach. In K. Berthiaume (Chair), Innovations in Methodological Approaches in Creativity Assessments for Gifted Education. Symposium to be presented at the annual meeting of the American Educational Research Association, San Diego, CA.
• Grajzel, K., Dumas, D., & Berthiaume, K. (2022). Time spent on task positively predicts creative quality of responses for elementary students. Roundtable talk accepted for presentation at American Educational Research Association Annual Meeting, San Diego, CA.
• Acar, S., Berthiaume, K., Grajzel, K., & Flemister, T. (2021). Automated scoring of Torrance Tests of Creative Thinking (TTCT) verbal form for originality. Combined session at National Association for Gifted Children Annual Conference.