Paper Info
Title
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.
Year
DOI
Venue
2018
10.1145/3303772.3303781
Proceedings of the 9th International Conference on Learning Analytics & Knowledge
Keywords
Field
DocType
Detailed Access Trajectory, Massive Open Online Course, learning pattern, marginalized learner, representation learning
Data science,Clickstream,Information retrieval,Computer science,Massive open online course,Granularity,Unit of time,Feature learning,Trajectory,Binary number,Student learning
Journal
Volume
Citations 
PageRank 
abs/1812.05767
0
0.34
References 
Authors
16
4
Name
Order
Citations
PageRank
Yanbang Wang192.81
Nancy Law2166.13
Erik Hemberg314335.68
Una-May O'Reilly41477181.38