LAK'17 Workshops and Tutorials

Quasi-Experimental Design for Causal Inference Using Python and Apache Spark: A Hands-on Tutorial

The 7th International Learning Analytics & Knowledge Conference

March 13-17, 2017

OBJECTIVES

A hands-on workshop, focused on practical skill-building.



CASUAL INFERENCE

Learn about how to make causal inferences in the context of educational research with big data.


QUASI-EXPERIMENT DESIGN

Implement, evaluate and compare the quality of matching methods in quasi-experiment design.


PYTHON TOOLKIT

Use the Python code provided on GitHub as a toolkit to make causal inferences in your research.


APACHE SPARK

Utilize Apache Spark to perform simple causal inference on big data in parallel.

MOTIVATION


Educational practitioners and policy makers require evidence, supporting claims about educational efficacy. Evidence is often found using causal relationships between education inputs and student learning outcomes. Causal inference covers a wide range of topics in education research, including efficacy studies to prove if a new policy, software, curriculum or intervention is effective in improving student learning outcomes. Randomized controlled trials (RCT) are considered a gold standard method to demonstrate causality. However, these studies are expensive, timely and costly, as well as not being ethical to conduct in many educational contexts.

Causality can also be deducted purely from observational data. In this tutorial, we will review methodologies for estimating the causal effects of education inputs on student learning outcomes using observational data. This is an inherently complex task due to many hidden variables and their interrelationships in educational research. In this tutorial, we discuss causal inference in the context of educational research with big data.

This is the first tutorial of its kind at Learning Analytics and Knowledge Conference (LAK) that provides a hands-on experience with Python and Apache Spark as a practical tool for educational researchers to conduct causal inference. As a prerequisite, attendees are required to have familiarity with Python.


AGENDA

A hands-on workshop, focused on practical skill-building.



  • INTRODUCTION TO CASUAL INFERENCE

    Learn about how to make causal inferences in the context of educational research with big data.


  • MATCHING METHODS FOR CASUAL INFERENCE

    Implement, evaluate and compare the quality of matching methods in quasi-experiment design.


  • HANDS-ON EXERCISES

    Build your Python workflow to make causal inferences in your research.


  • CASUAL INFERENCE IN BIG DATA USING APACHE SPARK

    Utilize Apache Spark to perform simple causal inference on big data in parallel.

  • YOUR CASUAL
    INFERENCE
    TOOLKIT

OUR AMAZING TEAM

Part of Data Science Team at McGraw-Hill Education Digital Platform Group.




Alfred Esssa

VP Analytics and R&D




Jie Zhang

Sr. Software Engineer




Shirin Mojarad

Data Scientist




Nicholas Lewkow

Data Scientist




Our team will help you to build your educational predictive model using Python.

CONTACT US

Ask us any questions you have about workshop.