CAREER: A Rational Analysis of How Teachers’ Examples Constrain Learning and Inference

Principal Investigator: 
Project Overview
Background & Purpose: 

The proposed research investigates the hypothesis that socio-pedagogical context affects learning. Previous research show that both children and adults draw different inferences from the same data depending on the socio-pedagogical context---whether the informant is knowledgeable, helpful, and intends to teach. Rational analysis of pedagogical learning explains these differences and generates a number of predictions about how inferences should differ in novel pedagogical and non-pedagogical situations.

The proposed work specifically investigates predictions about how pedagogically-sampled (or non-pedagogically-sampled) examples affect trust in the teacher and affect subsequent learning, and how these social-pedagogical inferences can be leveraged to facilitate learning in richly-structured STEM domains.

Setting: 

University of Louisville

Research Design: 

The project is using a cross-sectional research design and will generate evidence that is causal [experimental, computational modeling] and synthetic [computational modeling]. Lab experiments are being used to collect data on young children and college undergraduates. The study is comparing a pedagogical sampling of data with a non-pedagogical sampling of data. Analysis plans include standard statistical methods such as binomial tests, chi-squared, and t-tests, as well as computational modeling.

Findings: 

Findings will be posted as they become available.

Publications & Presentations: 

All publications resulting from this grant will be posted on the University of Louisville Computational Cognitive Science lab website at http://louisville.edu/psychology/shafto.