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Towards Automatic Collaboration Analytics for Group Speech Data Using Learning Analytics.

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Summary
This summary is machine-generated.

This study introduces a new method for analyzing co-located collaboration (CC) by focusing on conversation content, not just speech patterns. The tool visualizes word linkages to reveal conversational richness, advancing automatic collaboration analytics.

Keywords:
co-located collaboration analyticscollaborationcollaboration analyticsgroup speech analyticsmultimodal learning analytics

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Computational Social Science

Background:

  • Co-located collaboration (CC) analytics traditionally focus on speech patterns (how people talk).
  • Limited research explores the content (what people talk about) and its richness in CC.
  • Existing content analysis methods are often lab-based and lack dynamic visualization of word linkages.

Purpose of the Study:

  • To prototype a tool for automatic collaboration analytics by analyzing the content of conversations.
  • To move beyond simple speech features and assess the richness of communication in co-located settings.
  • To visualize word and phrase linkages interactively to understand conversational depth.

Main Methods:

  • Designed and implemented a technical setup for automatic audio data collection, processing, and visualization.
  • Conducted field trials with university staff playing a board game with assigned roles.
  • Employed word-level analysis, network graphs for turn-taking and word linkage visualization, and centrality measures.

Main Results:

  • Developed a partially automated system for analyzing conversational content and richness.
  • Visualized the strength of linkages between words and phrases, revealing conversational depth.
  • Identified limitations in automated speaker diarization and text pre-processing.

Conclusions:

  • The developed approach offers a way forward for understanding the richness of conversations in different roles.
  • This work represents a significant step towards fully automated collaboration analytics.
  • Partially automated analysis of conversational content provides valuable insights into group dynamics.