Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Project Overview
The document explores the integration of generative AI in education, emphasizing its potential to enhance teaching practices and improve student learning outcomes, particularly in mathematics. It underscores the importance of teacher-student interactions within AI-supported classrooms, revealing that effective teacher practices significantly influence learning rates. Through the use of quantitative ethnography methods, such as Ordered Network Analysis (ONA), the study analyzes behaviors of both teachers and students, providing valuable insights into the dynamics of classroom interactions. Notably, the findings indicate that incorporating out-of-tutor data can greatly improve the understanding of student learning rates, highlighting the necessity of tailored teacher support, especially for students exhibiting lower learning rates. Overall, the document illustrates the transformative impact of generative AI on educational practices, suggesting that strategic teacher involvement is crucial in leveraging AI tools to foster better learning environments and outcomes.
Key Applications
AI tutors and learning analytics
Context: 7th-grade mathematics classrooms using AI-based tutoring systems
Implementation: The study observed teacher practices and student interactions over three days, capturing data from AI tutor logs, classroom observations, and teacher position tracking.
Outcomes: Improved understanding of effective teacher practices that enhance student learning rates; insights into how teacher monitoring and communication affect student engagement.
Challenges: Integrating and analyzing multimodal data is complex; teacher practices vary widely, making standardization difficult.
Implementation Barriers
Technical
Challenges in processing, annotating, and analyzing multimodal data for meaningful insights.
Proposed Solutions: Utilizing quantitative ethnography methods to integrate different data sources and enhance interpretability.
Practical
Teachers may lack resources (time, attention) to change their practices based on analytics.
Proposed Solutions: Implementing teacher-facing analytics tools that minimize the burden on teachers while providing actionable insights.
Project Team
Conrad Borchers
Researcher
Yeyu Wang
Researcher
Shamya Karumbaiah
Researcher
Muhammad Ashiq
Researcher
David Williamson Shaffer
Researcher
Vincent Aleven
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Conrad Borchers, Yeyu Wang, Shamya Karumbaiah, Muhammad Ashiq, David Williamson Shaffer, Vincent Aleven
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai