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Visualizing learner engagement, performance, and trajectories to evaluate and optimize online course design.

Michael Ginda1, Michael C Richey2, Mark Cousino2

  • 1Department of Intelligent Systems Engineering, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.

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Summary

Learning analytics and visualizations offer insights into learner engagement and performance in online courses. This study introduces metrics and visualizations to identify learning pathways and optimize course design for effective workforce training.

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Data Visualization

Background:

  • Online courses require effective design to optimize learner engagement and performance, especially in workforce training.
  • Existing methods may not fully capture the dynamic aspects of learner behavior and progression through complex course structures.

Purpose of the Study:

  • To introduce novel metrics and visualizations for analyzing learner engagement, performance, and trajectories in online learning environments.
  • To identify prototypical learner behaviors and learning pathways within online courses.
  • To provide actionable insights for course designers and instructors to evaluate and enhance course effectiveness.

Main Methods:

  • Development of a set of metrics to capture dynamical aspects of learner engagement, performance, and trajectories.
  • Application of these metrics to identify prototypical behaviors and learning pathways through course content, activities, and assessments.
  • Empirical validation using over 30 million logged events from 1,608 engineers in a complex systems online course.

Main Results:

  • Visualization of course structure and learner interaction patterns with course materials, activities, and assessments.
  • Tree visualizations effectively represent hierarchical course structures and content module sequences.
  • Learner trajectory networks reveal individual learning pathways, engagement patterns, content access strategies, and performance.

Conclusions:

  • The proposed metrics and visualizations provide valuable evidence for evaluating online course usage and effectiveness.
  • This approach supports instructors and designers in refining course materials and intervention strategies for better learning outcomes.
  • The study demonstrates the utility of learning analytics and visualization in optimizing online workforce training.