Using Detailed Access Trajectories for Learning Behavior Analysis
This work addresses the need for better data representations in MOOC learning analytics, but it is incremental as it builds on existing data types without introducing a new method.
The authors tackled the problem of analyzing student learning behavior in MOOCs by introducing detailed access trajectories (DATs), a data organization between clickstream and coarse aggregates, and found that DATs contain rich information about learning behaviors.
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, e.g. days and the other that is a chronologically ordered list of a MOOC component type's instances, e.g. videos in instructional order. 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.