Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Project Overview
The document explores the integration of generative AI in educational settings, emphasizing its role in enhancing intelligent tutoring systems through Active Learning (AL) methods for Dialogue Act (DA) classification. It underlines the significance of sample informativeness in training classifiers, revealing that by employing statistical AL techniques, educators can effectively select more informative samples. This approach not only minimizes the costs associated with manual annotation but also boosts the performance of classifiers, ultimately leading to more effective and efficient educational tools. The findings suggest that leveraging generative AI can significantly advance the capabilities of intelligent tutoring systems, facilitating personalized learning experiences and improving overall educational outcomes.
Key Applications
Active Learning for Dialogue Act Classification
Context: Educational dialogue analysis for intelligent tutoring systems, targeting educators and researchers in AI and education.
Implementation: Utilizing statistical active learning methods to select informative training samples for the classification of dialogue acts in tutoring sessions.
Outcomes: Improved classifier performance with fewer training samples and reduced costs for manual annotation.
Challenges: The challenge of obtaining high-quality annotated samples and ensuring the classifier learns effectively from them.
Implementation Barriers
Operational
Manual annotation of dialogue acts is time-consuming and costly.
Proposed Solutions: Employing statistical active learning methods to reduce the need for extensive manual annotations by selecting the most informative samples.
Technical
Low informativeness in the majority of annotated samples can hinder the learning process of the classifier.
Proposed Solutions: Investigating and applying active learning methods to ensure a focus on high-informativeness samples.
Project Team
Wei Tan
Researcher
Jionghao Lin
Researcher
David Lang
Researcher
Guanliang Chen
Researcher
Dragan Gasevic
Researcher
Lan Du
Researcher
Wray Buntine
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Wei Tan, Jionghao Lin, David Lang, Guanliang Chen, Dragan Gasevic, Lan Du, Wray Buntine
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