Computational Discourse Analysis

This project focuses on developing methodological innovations to analyze learning engagement and collaborative behaviors, with the goal of generating actionable insights that support instructors in designing and facilitating more effective learning experiences. This work incorporates techniques like NLP, LLMs, and network analysis to better understand student learning and collaboration.

Publications and Updates

• [Conference] Jung, Y., Zhu, X., Oshima, J., Oshima, R., Chen, B., Moon, J., McNeill, L., Edmonds, C., Banihashem, K., Noroozi, O., Dey, I., Puntambekar, S., & Jackson, T. J. (2025). Towards Actionable Collaborative Discourse Analysis: Bridging Advanced Computational Analysis With Practical Implementation. In Oshima, J., Chen, B., Vogel, F., & Järvelä, J. (Eds.), Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning - CSCL 2025 (pp. 472-480). International Society of the Learning Sciences.
  
• [Conference] Zhu, X., & Chen, B. (2023). Understanding idea creation in collaborative discourse through networks: The Joint Attention–Interaction–Creation (AIC) framework. Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023.
  
• [Conference] Chen, B., Zhu, X., & Shui, H. (2022). Socio-semantic network motifs framework for discourse analysis. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 500-506).