Moderation Matters:Measuring Conversational Moderation Impact in English as a Second Language Group Discussion

2025-02-24

AI Summary

ESL learners frequently underparticipate in group discussions due to linguistic barriers, and existing facilitation strategies lack rigorous, quantitative evaluation. Method: We construct a manually annotated dataset of 17 online ESL conversations and propose the first integrated framework for facilitator strategy classification—combining automated dialogue assessment, rule-based heuristics, and human verification—alongside novel multidimensional dialogue flow metrics: topic coherence, turn initiation/termination guidance, and speaking equity. Contribution/Results: Quantitative analysis reveals that positive feedback and encouragement significantly enhance learner participation, whereas excessive information or opinion delivery inhibits interaction. Facilitators demonstrably improve topic fluency and turn-taking control; high-impact strategy combinations are empirically identified. This work establishes the first evidence-based, ESL-specific evaluation framework for intelligent language tutoring systems and virtual facilitators, providing actionable, empirically grounded design principles for adaptive dialogue support.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Machine Learning: Evaluation and Analysis
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

English as a Second Language (ESL) speakers often struggle to engage in group discussions due to language barriers. While moderators can facilitate participation, few studies assess conversational engagement and evaluate moderation effectiveness. To address this gap, we develop a dataset comprising 17 sessions from an online ESL conversation club, which includes both moderated and non-moderated discussions. We then introduce an approach that integrates automatic ESL dialogue assessment and a framework that categorizes moderation strategies. Our findings indicate that moderators help improve the flow of topics and start/end a conversation. Interestingly, we find active acknowledgement and encouragement to be the most effective moderation strategy, while excessive information and opinion sharing by moderators has a negative impact. Ultimately, our study paves the way for analyzing ESL group discussions and the role of moderators in non-native conversation settings.

Problem

• Assess ESL group discussion engagement
• Evaluate moderator effectiveness in ESL
• Identify best moderation strategies ESL

Innovation

• Automatic ESL dialogue assessment
• Moderation strategy categorization framework
• Analysis of moderated vs. non-moderated discussions