ArgueTutor: An Adaptive Dialog-based Learning System for Argumentation Skills

Document Title

ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills

Document Information

• Subject Area: Educational Technology and Human-Computer Interaction
• Keywords: Educational applications, teaching dialogue agents, argumentation learning, adaptive learning, natural language processing, machine learning, information technology, dialogue systems, metacognitive skills, intelligent tutoring systems

Research Background and Issues

• Problems or Challenges Identified by the Authors:

  • Students need to continuously improve metacognitive skills such as critical thinking, collaboration, and problem-solving, especially structured and logical argumentation skills.
  • Educational institutions face challenges in providing personalized tutoring due to time constraints, large-scale lectures, and the prevalence of remote learning.
  • Professional teacher resources for individual tutoring are scarce, particularly in large-scale online courses.

• Importance:

  • Argumentation skills are a crucial component of daily communication and solving complex problems, serving as a foundational element for democratic civic discourse. These skills are not only academically significant but increasingly vital for future workplaces.

• Research Motivation and Related Work:

  • The field of educational technology, centered on natural language processing (NLP) and machine learning (ML), is rapidly evolving.
  • Existing tools support argumentation learning but lack intelligent feedback and dialog-based learning design.
  • Dialog-based learning systems, such as teaching dialogue agents, have shown remarkable results in other domains but are underutilized in argumentation learning.

Solution

• Proposed Solution:

  • Develop an adaptive dialog-based argumentation learning system named ArgueTutor. It provides students with personalized, real-time feedback, theoretical support, and step-by-step guidance through a dialog interface.
  • Utilize argument mining techniques to automatically analyze the quality of student texts and offer intelligent feedback based on dialog-based learning design.

• Innovations:

  • ArgueTutor is the first intelligent dialog-based tutoring system focused on argumentation skills learning, offering real-time, adaptive feedback powered by NLP and ML.
  • The system integrates student needs with theory-driven design principles to enable interactive independent learning.

• Implementation Steps and Technologies:

  1. Designing the Dialog Interface:

     • Use theory-driven and user-centered methods to design an interactive and simple interface.
     • Provide staged learning guidance (e.g., task explanations, difficulty analysis, theoretical input).

  2. Developing the Argument Mining Model:

     • Train the model using a corpus of student feedback texts annotated with argument components.
     • Employ deep learning methods based on the BERT architecture to identify argument structures in texts (e.g., "claim" and "premise").

  3. Setting Up Comparative Experiments:

     • Compare the effectiveness of ArgueTutor with traditional non-automated discussion script tools.

Research Findings

• Specific Findings:

  • Students using ArgueTutor produced texts with significantly higher argumentation quality compared to those using traditional tools.
  • Participants found ArgueTutor easy to use and more enjoyable to interact with, demonstrating a positive combination of real-time feedback and learning motivation.

• Advantages:

  • Compared to static discussion script tools, ArgueTutor improved the formal argumentation quality and persuasiveness of students' texts.
  • An experiment showed that the interactive learning model based on the ICAP framework enhanced student engagement and learning outcomes.

• Experimental or Evaluation Results:

  • Formal Argumentation Quality:
    • Students using ArgueTutor wrote an average of 3.56 supporting arguments, significantly higher than the 2.64 produced using traditional tools.
  • Perceived Argumentation Quality:
    • Texts by ArgueTutor users were rated with a persuasiveness score of 3.48, compared to 2.80 for traditional tools.
  • Ease of Use and Enjoyment:
    • ArgueTutor scored 3.73 for ease of use and 3.41 for learning enjoyment, significantly outperforming traditional tools.

• Limitations and Future Directions:

  • Limitations:

    • The experiment sample was limited to one university, which may restrict generalizability.
    • Long-term effects of using the system have not been explored, and novelty effects may exist.

  • Future Directions:

    • Optimize feedback granularity, such as visualizing relationships between argument components.
    • Provide transparent explanations of feedback mechanisms.
    • Extend ArgueTutor to other domains and languages (e.g., English corpora).
    • Conduct longitudinal studies to assess the long-term effects of using the tool.