Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
Project Overview
The document explores the integration of generative AI in education, specifically through the enhancement of educational dialogue act classifiers aimed at improving intelligent tutoring systems. It emphasizes the development of these classifiers to effectively operate in low-resource and imbalanced data environments, utilizing AUC maximization techniques for better performance. The research demonstrates the importance of automating the identification of dialogue acts within tutoring dialogues, which is crucial for optimizing educational interactions and outcomes. Findings indicate that AUC maximization methods significantly surpass traditional cross-entropy approaches in their ability to manage imbalanced datasets, thus reinforcing the potential of generative AI to transform educational practices by facilitating more effective and responsive tutoring systems.
Key Applications
Dialogue Act Classification
Context: Educational context focusing on tutoring dialogues, targeted at both tutors and students
Implementation: Utilized machine learning models to classify dialogue acts based on labeled sentences from tutoring dialogues, optimizing with AUC maximization instead of traditional cross-entropy loss
Outcomes: Improved classification performance and robustness of dialogue act classifiers under low-resource and imbalanced data conditions
Challenges: Limited labeled data leading to imbalanced class distribution, which impacts the performance of classifiers, especially for minority classes
Implementation Barriers
Data-related and Resource-related barrier
Imbalanced class distribution in training datasets due to limited labeled data, leading to poor performance on minority classes and challenges in low-resource scenarios.
Proposed Solutions: Adopt AUC maximization approaches to enhance classifier robustness to imbalanced data distribution and improve performance under low-resource conditions.
Project Team
Jionghao Lin
Researcher
Wei Tan
Researcher
Ngoc Dang Nguyen
Researcher
David Lang
Researcher
Lan Du
Researcher
Wray Buntine
Researcher
Richard Beare
Researcher
Guanliang Chen
Researcher
Dragan Gasevic
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Jionghao Lin, Wei Tan, Ngoc Dang Nguyen, David Lang, Lan Du, Wray Buntine, Richard Beare, Guanliang Chen, Dragan Gasevic
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