Using Detailed Access Trajectories for Learning Behavior Analysis

ABSTRACT

Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine granularity of the clickstream and coarse organizations that count activities, aggregate students or use long duration time units. A detailed access trajectory (DAT) consists of binary values and is two dimensional with one axis that is a time series, and the other that is a chronologically ordered list of a MOOC component type's instances, videos in instructional order, for example. Most popular MOOC platforms generate data that can be organized as detailed access trajectories (DATs). We explore the value of DATs by conducting four empirical mini-studies. Our studies suggest DATs contain rich information about students' learning behaviors and facilitate MOOC learning analyses.

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Index Terms

• Applied computing

  • Education

    • E-learning

• Human-centered computing

  • Visualization

    • Visualization application domains

      • Visual analytics

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Clustering and classification

  • Information systems applications

    • Data mining