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A multimodal experimental dataset on agile software development team interactions.

Diego Miranda1, Carlos Escobedo1, Dayana Palma1

  • 1Escuela de Ingeniería Informática, Unviersidad de Valparaíso, Valparaíso, Chile.

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Summary
This summary is machine-generated.

This study introduces a new multimodal dataset for analyzing collaborative dynamics in agile development teams. It captures verbal and non-verbal communication to understand team effectiveness and decision-making.

Keywords:
Agile developmentBehavioural synchronyCollaborative learningMultimodal analyticsTeam dynamicsUser stories

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Psychology

Background:

  • Analyzing collaborative dynamics in agile teams requires rich, multimodal data.
  • Existing datasets often lack the depth to capture nuanced team interactions.
  • Simulated teamwork environments are crucial for controlled data collection.

Purpose of the Study:

  • To present a novel multimodal dataset of simulated agile development teams.
  • To facilitate the study of verbal and non-verbal communication in teamwork.
  • To support research on factors influencing team effectiveness and decision-making.

Main Methods:

  • Collected data from 19 simulated agile teams (76 participants) during two collaborative activities (with and without Planning Poker).
  • Utilized MediaPipe, YOLOv8, and DeepSort for capturing non-verbal behaviors (posture, expressions, attention, gestures).
  • Incorporated audio recordings, automatic transcriptions (WhisperX), attention logs, mimicry labels, and surveys.

Main Results:

  • The dataset provides a comprehensive view of collaborative behavior in agile contexts.
  • It enables qualitative analysis of interactions and development of predictive performance models.
  • Explores the influence of shared visual attention and behavioral synchrony on team outcomes.

Conclusions:

  • This multimodal dataset offers a unique resource for researchers in various fields.
  • It advances the understanding of team dynamics through integrated analysis of communication modalities.
  • Facilitates the development of AI models for predicting group performance in agile settings.