ERP Markers for Conventional and Unconventional Number Gestures
The goal of this project is to characterize the ERPs for automatic processing of number gestures. Previous behavioral research has shown that finger counting and montring habits influence recognition of number gesture configuration. We explore the ERP differences in recognition of different number gesture configurations (counting, montring, unconventional). The task involves identifying different number gestures, one condition showing conventional number gesture representations (e.g., index, middle, and ring fingers up for number “3”), and the second showing unconventional gestures (thumb, index, and little fingers up for number “3”). The analysis focus on the differences between the temporal aspects of processing these different gestures, one automatic the other non-automatic.
- Soylu, F. (in review). ERP differences in processing canonical and non-canonical finger numeral representations.
- Rivera, B., Shannon, N., & Soylu, F. (2017). ERP Markers for Number Gesture Processing. Poster presented at the The National Diversity in STEM Conference. Salt Lake City, UT: October, 2017
The Relation between Fingers and Arithmetic Processing
This project is a collaboration with Dr. Sharlene Newman's Cognitive Neuroimaging Lab at Indiana University. We study how children's finger skills skills correlate with and predict arithmetic skills, and the neural mechanisms underlying this relation.
- Soylu, F., & Newman, S.D. (in review). Gray Matter Correlates of Finger Gnosis in Children: a VBM Study
- Soylu, F., Lester, F.K., & Newman, S.D. (in press). You can count on your fingers: The role of fingers in early mathematical development. Journal of Numerical Cognition.
- Soylu, F., Newman, S.D. (2016). Anatomically ordered tapping interferes more with one-digit addition than two-digit addition - A dual-task fMRI study. Cognitive Processing. 17(1), 67–77
- Newman, S.D., & Soylu F. (2014). The impact of finger counting habits on arithmetic in adults and children. Psychological Research, 78(4), 549–56
- Soylu, F. & Newman, S. D. (2011). Is arithmetic embodied? Differential interference of sequential finger tapping on addition during a dual-task paradigm. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
- Soylu, F. (2011). Mathematical cognition as embodied simulation. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
This project is a collaboration with Dr. Lisa Hsin's ECS Lab. Our goal is to explore mathematical development of and number processing in bilingual children and adults.
Fraction operations are difficult, specially for children. Being able to understand fractions requires the development of intuitions not carried by natural numbers. One strategy that has been successful in developing fraction understanding in children has concentrated in making the link between the notation of fractions and their numerical value. The numerical value of a fraction is called the fraction's magnitude. This study looks at the ERP components of fractions' magnitude processing to identify the strategies that successfully employ fraction magnitude to solve math problems.
- *Rivera,B., & Soylu, F. (2017). The Arithmetic N400 in Fraction Processing. Poster presented at the College of Education 9th Annual ESPRMC Graduate Symposium. The University of Alabama, Tuscaloosa: March, 2017
Predictive Modeling of Educational Interventions
This project is a collaboration with Drs. Hyemin Han and Kangwook Lee. We explore how different modeling and machine learning techniques can be applied to predict outcomes of large-scale learning interventions based in small-scale implementation data.
- Han, H., Lee, K., & Soylu, F. (in review). Applying the Deep Learning Method for Simulating Long-term outcomes of Educational Interventions
- Han, H., Lee, K., & Soylu, F. (2018). Simulating outcomes of interventions using a multipurpose simulation program based on the evolutionary causal matrices and Markov chain. Knowledge and Information Systems, 18(2), 223-227. doi:10.1007/s10115-017-1151-0
- Han, H., Lee, K., & Soylu F. (2016). Predicting long-term outcomes of educational interventions using the evolutionary causal matrices and Markov chain. Trends in Neuroscience and Education, 5(4), 157–165.
Computational Thinking in STEM Learning
Given that computational modeling has revolutionized all fields of science, acquiring computational thinking skills is an essential aspect of STEM learning. In the ModelSim project (PI: Uri Wilensky, Northwestern) we studied scaled implementation of computational modeling units in high school science classes in the Chicagoland area.
- Soylu, F., Holbert, N., Brady, C., & Wilensky, U. (2017). Embodied perspective taking in learning about complex systems. Journal of Interactive Learning Research, 28(3), 269–303.
- Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015). Sandboxes for model-based inquiry. Journal of Science Education and Technology, 24(2-3), 265-286.
- Holbert, N., Brady, C., Soylu, F., Novak, M., Wilensky, U. (2015). The model gallery: supporting idea diffusion in computational modeling activities. Poster presentation at the 2015 AERA (American Educational Research Association) Annual Meeting (SIG - Advanced Technologies for Learning), Chicago, IL: April, 2015.