Rui Meng

Ph.D. Student, Educational Psychology

LUCID Research Project: OMELET

Cognitive Development and Communication Lab

Research Advisors: Martha Alibali and Percival Matthews

Research Projects:

My research addresses questions at the intersection of cognitive, developmental, and educational science, currently focusing on mathematics.

Spatial Representations of Symbolic Fractions and Nonsymbolic Ratios

One special property of the magnitudes of symbolic fractions and nonsymbolic ratios is that their magnitudes are determined relationally. They offer new potential insights into the nature of human numerical and mathematical cognition. One study demonstrated the Spatial-Numerical Association of Response Codes (SNARC) effect with nonsymbolic ratios. Another study investigated number line estimation patterns of symbolic fractions and nonsymbolic ratios from aspects of percent absolute error (PAE), psychophysical functions, and bias pattern.

Interpretation of Covariation Data

Decision making depends on our ability to draw appropriate causal inferences from information about covariation between events. Recent work suggest that variable symmetry influences how people interpret covariation data and draw causal inferences from a 2 x 2 contingency table. Symmetrical variables support correct interpretations more than asymmetrical variables. In this project, I study how symmetry of both variables would influence covariation data interpretation and what educational interventions can support successful covariation data interpretation.

Optimizing Human Learning with Machine Teaching

A long-standing but elusive goal in machine-aided education has been to exploit cognitive models of human learning to select teaching or practice experiences for students that will efficiently lead them toward the desired knowledge state. The project shows how contemporary optimization methods allow theorists to discover, for any implemented learning model and desired outcome, an optimal teaching set – that is, a model training set that most efficiently produces the desired outcome given the model. The approach is currently being tested with arithmetic domain, but has the potential to boost human learning in other important educational domains.