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Towards Automatic Collaboration Analytics for Group Speech Data Using Learning Analytics
Sambit Praharaj1, Maren Scheffel2, Marcel Schmitz1,3
1Educational Science Faculty, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
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.
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.
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