ELAT: EdX Logfile Analysis Tool

Simple browser-based tool to process events from edX logfiles into a local database for analysis

Quick Start

What is ELAT?

ELAT is a simple, browser-based, fully local processing tool that can take the logfiles with events produced by edX and generate a database that can be downloaded as csv files, along with some basic indicators of students in a given edX course. It is an ongoing project developed by the Delft Data Science (DDS) Initiative, for Learning Analytics research from the Web Information Systems (WIS) Group together with the Centre for Education and Learning (CEL) and the TU Delft Extension School. You can start using ELAT simply by going to the workbench and uploading metadata files and logfiles (to your own browser, there is no server involved). The code is fully available here.

Why use ELAT?

Session Focused

ELAT considers a student's learning session as a series of interactions with course material such as mouse clicks, video controls or problem responses. ELAT processes sessions of different types, for example: a video interaction session starts when a student opens a video, and it contains the duration, number of pauses, rewinds, fast forwards, etc., until the video is closed.

Easy and Private

Get started by uploading the metadata files of the course, then the logfiles of the corresponding time period. Once processed, the csv files for each type of session can be downloaded. No account required, everything stays in your computer and everything can be deleted with a single button.

Primary Indicators

From the information extracted from the logfiles, ELAT automatically generates a few general indicators, such as the number of students per day, and the duration of their sessions. While these indicators provide a helpful overview on the processed information, it's just one of many use cases of ELAT in Learning Analytics, the main value is in the generated csv files.


We are proud to announce that this work will be part of the 10th Learning Analytics and Knowledge Conference (LAK20) in Frankfurt as edX Log Data Analysis Made Easy. If you use ELAT, or adapt it for your work, please cite our (preprint) publication.
             author = {Valle Torre, Manuel and Tan, Esther and Hauff, Claudia},
             title = {edX Log Data Analysis Made Easy},
             booktitle = {Proceedings of the 10th International Conference on Learning Analytics \& Knowledge},
             series = {LAK20},
             year = {2020},
             location = {Frankfurt, Germany}