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.
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.
Dumas, D., 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., & Dumas, D. (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, 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 Instruction, 42, 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), 592-608. http://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, D., Schmidt, L. C., & Alexander, P. A. (2016). Predicting creative problem solving in engineering design. Thinking Skills and Creativity, 21, 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.
As an educational psychologist dedicated to understanding human learning processes, my teaching and research are inherently complementary.
For example, from my research, I know that the more and richer connections that students are able to map among class information, the better they will be able to access that information in memory, form justified beliefs and knowledge, and transfer that knowledge to novel contexts. However, I also know that, for many students, default behavior involves treating topics in a piecemeal fashion. Therefore, I continually strive to encourage students to construct meaningful connections among ideas.
I also believe deeply that all students deserve license to be creative in class. However, based on findings from the creativity literature, I know that thinking divergently or in novel ways can be uncomfortable for some students, who have been trained to generate only the “right” answer. In order to limit this tendency towards convergent thinking, I focus my grading on tasks that are open-ended, in which students can follow their interests and produce original work.
In general, I try to push students to think like psychologists, not just psychology students. Informed by the critical thinking literature, I know that this process requires careful analysis and informed evaluation of the work of scholars in the field. Therefore, I typically require students to interact with and critique empirical research in class.
Although I deeply value the time I spend on classroom instruction, in my view, some of the best opportunities for teaching and learning arise in advising and apprenticeship contexts, where I have the opportunity to guide a student through a complex process such as data analysis, writing, or corresponding with journal reviewers and editors. Therefore, I am excited for a variety of research collaborations with graduate students here at DU.
This portfolio last updated: Sep 14, 2017 2:43:25 PM