Towards Computer-Assisted Coding of Classroom Observations: A Computer Vision Approach to Measuring Positive Climate

Principal Investigator: Jacob Whitehill | Department of Computer Science | Worcester Polytechnic Institute

Principal Investigator(s):

Erin Ottmar | Social Science and Policy Studies | Worcester Polytechnic Institute

Summary

Dr. Whitehill (WPI), Dr. Ottmar (WPI), their collaborator Dr. Jennifer LoCasale-Crouch (UVa), and their graduate students are exploring how machine learning and computer vision algorithms can be used to characterize automatically the teacher-student and student-student interactions from classroom observation videos. Partially automating the process of classroom observation could facilitate finer-grined and more efficient feedback on teachers' classroom interactions, which could provide teachers with improved professional development feedback and researchers with a more powerful lens with which to measure the impact of educational interventions. As first steps, the researchers are investigating how automatically extractable features such as the facial expression of each participant (teacher, student), as well as auditory features of speech, can be analyzed by recurrent neural networks to predict positive climate and negative climate of the CLassroom Assessment Scoring System (CLASS). Later work will consider how to identify which participants are interacting with whom at each moment in time based on their audiovisual profiles, as well as how to combine machine-coded with human-coded CLASS scores to improve accuracy. This study harnesses a dataset of hundreds of CLASS-coded videos of toddler classrooms collected and annotated by the Center for the Advanced Study of Teaching and Learning at the University of Virginia.,

Grant Type:

Small Grant

Grant Amount:

$49,850.00

Year:

2018

Research Area:

Field-Initiated

Topic / Subject:

Early Childhood, Affect (e.g., Motivation), Technology

Methods / Approach:

Other - Quantitative, Observational research, Multivariate regression analysis/other regression analysis

Disciplinary Perspective:

Computer Science/Technology, Education