# Using Detailed Access Trajectories for Learning Behavior Analysis

## TL;DR

This paper introduces Detailed Access Trajectories (DATs), a new data organization method for MOOC learner activity that captures rich behavioral information at an intermediate granularity, enabling improved analysis of learning behaviors.

## Contribution

The paper proposes DATs as a novel data structure for MOOC activity analysis and demonstrates their usefulness through four empirical studies.

## Key findings

- DATs contain rich behavioral information
- DATs facilitate detailed MOOC learning analysis
- Empirical studies validate DATs' effectiveness

## 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, 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.