Spoken interaction modeling for automatic assessment of collaborative learning

Jennifer Smith, Harry Bratt, Colleen Richey, Nikoletta Bassiou, Elizabeth Shriberg, Andreas Tsiartas, Cynthia D’Angelo, Nonye Alozie

Research output: Contribution to journalConference article

Abstract

Collaborative learning is a key skill for student success, but simultaneous monitoring of multiple small groups is untenable for teachers. This study investigates whether automatic audiobased monitoring of interactions can predict collaboration quality. Data consist of hand-labeled 30-second segments from audio recordings of students as they collaborated on solving math problems. Two types of features were explored: speech activity features, which were computed at the group level; and prosodic features (pitch, energy, durational, and voice quality patterns), which were computed at the speaker level. For both feature types, normalized and unnormalized versions were investigated; the latter facilitate real-time processing applications. Results using boosting classifiers, evaluated by F-measure and accuracy, reveal that (1) both speech activity and prosody features predict quality far beyond chance using majority-class approach; (2) speech activity features are the better predictors overall, but class performance using prosody shows potential synergies; and (3) it may not be necessary to session-normalize features by speaker. These novel results have impact for educational settings, where the approach could support teachers in the monitoring of group dynamics, diagnosis of issues, and development of pedagogical intervention plans.

Original languageEnglish (US)
Pages (from-to)277-281
Number of pages5
JournalProceedings of the International Conference on Speech Prosody
Volume2016-January
StatePublished - Jan 1 2016
Event8th Speech Prosody 2016 - Boston, United States
Duration: May 31 2016Jun 3 2016

Keywords

  • Classroom education
  • Collaborative learning
  • Machine learning
  • Prosodic features
  • Speech activity detection
  • Student collaboration

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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  • Cite this

    Smith, J., Bratt, H., Richey, C., Bassiou, N., Shriberg, E., Tsiartas, A., D’Angelo, C., & Alozie, N. (2016). Spoken interaction modeling for automatic assessment of collaborative learning. Proceedings of the International Conference on Speech Prosody, 2016-January, 277-281.