• Research Interests

  • I am fascinated by human learning.  Today, nearly every educator inherently understands that different students seem to learn at different rates, or that different domains are better taught using differing instructional methods. But how do these profound differences come to be? How can we best leverage instructional resources to support the academic development of all students?

    In order to begin to address these questions, I apply advanced quantitative methodologies to educational data in an effort to scientifically understand the learning process: including when, how, and in whom learning occurs. Recently, I have worked to co-develop a novel methodical paradigm, called Dynamic Measurement Modeling (DMM) that may be used to quantify students’ learning capacity in a more valid way than previously used methods.

    I am also proud to be an investigator on an IES-funded grant to develop a new measure of original thinking for elementary school students based on text-mining metodology. I sincerely believe that creativity and originality are critically important mental attributes to support in students, and I am excited about advanced quantitative techniques for measuring creative attributes and modeling their development. 

    Moreover, I have closely studied two higher-order cognitive functions that allow individuals to flexibly adapt to shifting demands, and innovate around constraints and challenges: relational reasoning and creativity. In particular, I am interested in the reliable and valid measurement of these constructs, as well as the role they play in student success, especially in STEM fields. For example, I have studied how medical students and residents use relational reasoning to arrive at clinical diagnoses, as well as how engineering students creatively solve mechanical problems.

  • Curriculum Vitae

    Dumas_CV_September2020.pdf

  • Selected Recent Papers

  • Dynamic Measurement Modeling

    Dumas, D., McNeish, D., & Greene, J. A. (2020). Dynamic measurement: A theoretical–psychometric paradigm for modern educational psychology. Educational Psychologist, 55(2), 88–105. https://doi.org/10.1080/00461520.2020.1744150

    McNeish, D., Dumas, D., & Grimm, K. (2020). Estimating new quantities from longitudinal test scores to improve forecasts of future performance. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1691484

    Dumas, D., McNeish, D., Sarama, J., & Clements, D. (2019). Pre-school mathematics intervention can significantly improve student learning trajectories through elementary school. AERA Open. https://doi.org/10.1177%2F2332858419879446

    Dumas, D., McNeish, D., Schreiber-Gregory, D., Durning, S., & Torre, D. (2019). Dynamic measurement in health professions education: Rationale, application, and possibilities. Academic Medicine. doi: 10.1097/ACM.0000000000002729

    McNeish, D. & Dumas, D. (2019). Scoring repeated standardized tests to estimate capacity, not just current ability. Policy Insights from the Brain and Behavioral Sciences. [invited paper].  https://doi.org/10.1177/2372732219862578

    McNeish, D., & Dumas, D. (2018). Calculating the conditional reliability of dynamic measurement modeling capacity estimates. Journal of Educational Measurement. 55(4), 614-634.

    Dumas, D., & McNeish, D. (2018). Increasing the consequential validity of reading assessment using dynamic measurement modeling. Educational Researcher. 47(9), 612-614

    DumasD., McNeish, D., (2017). Dynamic measurement modeling: Using nonlinear growth models to estimate student learning capacity. Educational Researcher, 46(6), 284-292. DOI: 10.3102/0013189X17725747

    McNeish, D., & DumasD. (2017). Non-linear growth models as psychometric models: A second-order growth curve model for measuring potential. Multivariate Behavioral Research, 52, 61-85. https://doi.org/10.1080/00273171.2016.1253451

  • Dumas, McNeish, & Greene, 2020.pdf

  • Dumas, McNeish, Clements, Sarama, 2019.pdf

  • Dynamic_Measurement_in_Health_Professions.21.pdf

  • McNeish, Dumas, & Grimm (2019).pdf

  • McNeish & Dumas 2019 (PIBBS).pdf

  • McNeish & Dumas (2018).pdf

  • Dumas&McNeish2018.pdf

  • Dumas & McNeish 2017.pdf

  • McNeish & Dumas (2017) MBR.pdf

  • Creativity

    Dumas, D., Organisciak, P., Maio, S.*, Doherty, M. (in press). Four text-mining methods for measuring elaboration. Journal of Creative Behavior.

    Dumas, D., Organisciak, P., Doherty, M. (2020). Measuring originality with human raters and text-mining models: A psychometric comparison of methods. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000319

    Forthmann, B., Leveling, M.*, Dong, Y.*, Dumas, D. (2020). Investigating the quantity-quality relationship in scientific creativity with quantile regression: An empirical test of the tilted funnel hypothesis. Scientometrics. https://doi-org.du.idm.oclc.org/10.1007/s11192-020-03571-w

    Maio, S.*, Dumas, D., Organisciak, P., & Runco, M. (2020). Is the reliability of objective originality scores confounded by elaboration? Creativity Research Journal.

    Forthmann, B., Szardenings, C., Dumas, D. (2020). Testing the equal-odds theory in creativity research. Psychology of Aesthetics, Creativity, and the Arts.

    Dumas, D., & Dong, Y. (2019). Development and calibration of the Student Opportunities for Deeper Learning Instrument. Psychology in the Schools. https://doi.org/10.1002/pits.22292

    Forthmann, B., Paek, S., Dumas, D., Barbot, B. Holling, H. (2019). Scrutinizing the basis of originality in divergent thinking tests: On the measurement precision of response frequencies. British Journal of Educational Psychology. doi: 10.1111/bjep.12325

    Dumas, D. & Runco, M. (2018). Objectively scoring divergent thinking tests for originality: A re-analysis and extension. Creativity Research Journal. 30(4), 439-450.

    Dumas, D., Strickland, A. (2018). From book to bludgeon: A closer look at malevolent responses on the Alternate Uses Task. Creativity Research Journal. 30(4), 466-468.

    Dumas, D. (2018). Understanding high-school students’ perceptions of their learning opportunities: A doubly latent approach. Frontiers in Education: Educational Psychology.

    Dumas, D. (2018). Relational reasoning and divergent thinking: An examination of the threshold hypothesis using quantile regression. Contemporary Educational Psychology, 53, 1-196.

    Dumas, D., Schmidt, L. C., & Alexander, P. A. (2016). Predicting creative problem solving in engineering design. Thinking Skills and Creativity21, 50–66. http://doi.org/10.1016/j.tsc.2016.05.002

    Dumas, D., & Dunbar, K. N. (2016).  The creative stereotype effect. PLOS ONE. DOI: 10.1371/journal.pone.0142567

    Dumas, D., & Dunbar, K. N. (2014). Understanding fluency and originality: A latent variable perspective. Thinking Skills and Creativity, 14, 56-67.

  • Forthmann_et_al-2019-British_Journal_of_Educational_Psychology.pdf

  • Dumas & Dong, 2019.pdf

  • Dumas&Runco2018.pdf

  • Dumas2018b.pdf

  • Dumas&Strickland2018.pdf

  • Dumas2018.pdf

  • Dumas, Schmidt, & Alexander, 2016.pdf

  • Dumas&Dunbar2016.PDF

  • Dumas&Dunbar2014.pdf

  • Dumas&Alexander2018.pdf

  • Relational Reasoning

    Jablansky, S., Alexander, P. A., Compton, V., Dumas, D. (2019). The development of relational reasoning in primary and secondary school students: A longitudinal analysis. International Journal of Technology and Design Education.

    Dumas, D., Alexander, P.A. (2018). Assessing differential-item-functioning on the Test of Relational Reasoning. Frontiers in Education: Testing and Applied Measurement. https://doi.org/10.3389/feduc.2018.00014

    Dumas, D., Torre, D.M., Durning, S.J. (2018).  Using relational reasoning strategies to help improve clinical reasoning practice.  Academic Medicine. 93(5):709-714. doi: 10.1097/ACM.0000000000002114.

    Dumas, D. (2017).  Relational reasoning in science, medicine, and engineering. Educational Psychology Review. http://doi.org/10.1007/s10648-016-9370-6

    Grossnickle, E. M., Dumas, D., Alexander, P. A., & Baggetta, P. (2016). Individual differences in the process of relational reasoning. Learning and Instruction42, 141–159. http://doi.org/10.1016/j.learninstruc.2016.01.013

    Dumas, D., Alexander, P. A. (2016). Calibration of the Test of Relational Reasoning. Psychological Assessment, 28(10), 1303-1318. http://doi.org/10.1037/pas0000267

    Jablansky, S., Alexander, P.A., Dumas, D., & Compton, V. (2015). Development of relational reasoning in primary and secondary school students.  Journal of Educational Psychology, 108(4), 592-608http://doi.org/10.1037/edu000007

    Dumas, D., & Schmidt, L. (2015). Relational reasoning as predictor for engineering ideation success using TRIZ. Journal of Engineering Design. 26(3), 74–88

    Alexander, P. A., Dumas, D., Grossnickle, E. M., List, A., & Firetto, C. (2015). Measuring relational reasoning. Journal of Experimental Education, 83, 1-33.

    Dumas, D., Alexander, P. A., Baker, L. M., Jablansky, S., & Dunbar, K. N. (2014). Relational reasoning in medical education: Patterns in discourse and diagnosis. Journal of Educational Psychology, 106, 1021-1035. doi: 10.1037/a003677

    Dumas, D., Alexander, P. A., & Grossnickle, E. M. (2013). Relational reasoning and its manifestations in the educational context: A systematic review of the literature. Educational Psychology Review, 25, 391-427.

  • Dumas, Durning, & Torre, 2018.pdf

  • Dumas&Alexander2018.pdf

  • Dumas2016.pdf

  • Grossnickleetal2016.pdf

  • Dumas&Alexander2016.pdf

  • Jablansky et al 2015.pdf

  • Dumas&Schmidt2015.pdf

  • Alexander et al. 2015.pdf

  • Dumas, Alexander, Baker, Jablansky, Dunbar, 2014

  • Dumas, Alexander, Grossnickle, 2013.pdf

This portfolio last updated: 10-Sep-2020 10:29 AM