DR. MAAYAN PEREG

Computational Clinical Psychology lab Department of Psychology and Sagol school of Neuroscience

PI: Dr. Nitzan Shahar

Estimating individuals’ learning needs to improve the fit between students and their educational environment.

Learning in a classroom and following the teacher’s instructions is often difficult for some students. These students might perform better under self-guided learning environments that are led by their own interests and choices. Currently, it is extremely difficult for the educational system to adjust the instructional environment to individual students’ needs due to the high personnel and financial associated expenses. The project offers to develop a novel educational diagnostic tool that can identify the best learning environment for each individual, focusing on instruction-based vs. free-choice-based learning environment. We will develop a task that will allow us to estimate each student’s best-matched learning environment using artificial intelligence (AI) and reinforcement learning algorithms. Through the students’ interactions with the AI teacher, we will measure their ability to learn and act in each learning environment and determine which environment will maximize each student’s learning outcomes. We believe that this is a step forward to the development and application of advanced personalized educational systems.

About me

I hold an M.A and Ph.D. in Cognitive Psychology, and a B.A in Psychology and Art History, all from Ben-Gurion University. My interest in the Science of Learning has guided my research during my Ph.D. studies, where I investigated how individuals learn to follow instructions. My research involved testing the cognitive mechanisms underlying instructions-based behavior, as well as examining the influence of cognitive training on cognitive functioning. Currently, I am a postdoc at the Computational Clinical Psychology lab at TAU, where I am investigating individual learning characteristics in an attempt to facilitate personalized learning.