Instructor-in-the-Loop Exploratory Analytics to Support Group Work

Armanda Lewis, Xavier Ochoa, Rohini Qamra

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This case study examines an interactive, low barrier process, termed instructor-in-the-loop, by which an instructor defines and makes meaning from exploratory metrics and visualizations, and uses this multimodal information to improve a course iteratively. We present potentials for course improvement based on automated learning analytics insights related to students' participation in small active learning sessions associated with a large lecture course. Automated analytics processes are essential for larger courses where engaging smaller groups is important to ensure participation and understanding, but monitoring a large total number of groups throughout an instructional experience becomes untenable for the instructor. Of interest is providing instructors with easy-to-digest summaries of group performance that do not require complex set up and knowledge of more advanced algorithmic approaches. We explore synthesizing metrics and visualizations as ways to engage instructors in meaning making of complex learning environments, but in a low barrier manner that provides insights quickly.

Original languageEnglish (US)
Title of host publicationLAK 2023 Conference Proceedings - Towards Trustworthy Learning Analytics - 13th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Number of pages9
ISBN (Electronic)9781450398657
StatePublished - Mar 13 2023
Event13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023 - Arlington, United States
Duration: Mar 13 2023Mar 17 2023

Publication series

NameACM International Conference Proceeding Series


Conference13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023
Country/TerritoryUnited States


  • automated detection
  • group work
  • multimodal learning analytics

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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