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Workload Analytics

Modelling workloads to help students plan their term/program and administration to manage their course /program design.

Workload Analytics, a proof of concept developed by D2L Labs, explores how to model workload intensity (as a proxy for the stress that a user feels) and identify peak periods that need to be managed.

Individual learners can use the data to better select courses or training while maintaining a balanced workload. Learning/training providers can use the data when developing courses or programs to better account for peak periods that may negatively impact learner success.

Key Ideas

  • Differentiated intensity models

    Different types of projects and assignments have varied impacts on the user. For example, the intensity of a high stakes assessment, such as a final exam, has a different impact than a low stakes assessment, such as a weekly quiz or assignment. The system differentiates between these types of assessments.

  • Big picture view

    A dashboard view that displays a summary across a learner’s workload to help them understand when they will need to be more mindful of time management. For learning/training providers the dashboard provides a summary of the cumulative workload across learners to help them design programs that better support successful outcomes.

  • Optimize workloads

    Remove workload hotspots by changing the structure and timing of assessments to create a more balanced workload for learners.

  • Themes

    Learning in 2030
    Improving Mental Health

    Learn more
  • Technologies

    Machine Learning, Artificial Intelligence

  • Status

    Proof of Concept