Program Overview

Maximum Number of Students: 1

Where: A mix of in-person and remote work

Student Level: Rising sophomore or junior (2nd or 3rd year continuing student)

Coursework Requirements: No

Although theories of infant development stress the importance of everyday experiences for honing infants' motor skills, object knowledge, and language, the structure and content of infants' day-to-day lives is notoriously difficult to measure. Ideally, such measurements would simultaneously: 1) record rich data about multimodal experiences, 2) densely sample within a day and across days to capture variability in activities (e.g., errands, meals, play) over multiple timescales, 3) facilitate testing large, representative participant samples. Yet, no existing method satisfies all three criteria. Video observations in the home produce rich data, but are limited to a brief slice of time within a single day (usually while infants play). Day-long recording methods (wearable motion sensors, audio recorders) require specialized equipment, so are difficult to apply at scale. Daily diaries and surveys can broadly sample time and scale easily, but produce limited text response data. In this project, we will innovate a new method that employs caregiver-captured video to glean new insights about infants' everyday learning opportunities. We will apply the method to test new questions about how learning to walk alters infants' daily motor and object experiences. Leveraging the ubiquity of camera-enabled smartphones, we will prompt caregivers to take 5-s videos of their infants each hour of each day for two weeks (168 samples). We will apply a computer vision algorithm and train a machine learning classifier to automatically detect infant body position (upright, sitting, supine, and prone) from video to reduce the need for human coding of a large dataset.

Dr. Franchak will sponsor 1 student each year for 2023, 2024, and 2025 under his NSF Career Grant.

Eligibility

Email Dr. Franchak expressing your interest.