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On-the-spot student performance analysis through visualization

On-the-spot student performance analysis through visualization. American University of Beirut Wissam Nahas – eLearning Manager. Our Objective. Help instructor and administration track student weekly performance during semesters Give immediate feedback or advising in advance

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On-the-spot student performance analysis through visualization

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  1. On-the-spot student performance analysis through visualization American University of Beirut Wissam Nahas – eLearning Manager

  2. Our Objective • Help instructor and administration track student weekly performance during semesters • Give immediate feedback or advising in advance • Non-performant students require guidance or advising sessions in advance • Feedback at the end of the semester is not effective for recovery implementation • Loss of course learning outcomes

  3. Course Logs • Moodle is powerful in gathering detailed logs within a course. • The logs include: time, user’s full name, affected user, event context, event name, description, IP address, ...

  4. Log Limitations • The logs are detailed but not readable • The data is tabular • It lacks visualization • Visuals are expressive

  5. Work Flow

  6. Analytic Tool • Feed logs extracted from Moodle into analytic tool • Generate visuals • Through analysis of data based on the problem tackled • Charts help visualize patterns in data

  7. Visuals • Patterns can be seen in behavior, performance, access, interactions, posting, submissions, navigation, and level of engagement • Visuals can be of different kinds depending on the data

  8. Visuals Implemented • Participation is based on behavior in events and navigation to different events. • Contribution is based on posting to the forum and submissions of assignments and quizzes.

  9. Programming language for statistical computing and graphics https://rstudio.cloud

  10. Advantages • Flexible • Free • Easy access • User - friendly

  11. Instructor Point of View • Tracking progress • Tracking contribution • Monitoring participation • Catching cheating in exams

  12. Live Demo

  13. What’s Next? • Synchronization between Moodle and the Analytic Tool • Incorporate more visuals depending on instructor need and data analysis • Implement cheating detection • Apply machine learning to predict student Grade

  14. Thank You!

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