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.
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INTRODUCTION TO CASUAL INFERENCE
Learn about how to make causal inferences in the context of educational research with big data.
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MATCHING METHODS FOR CASUAL INFERENCE
Implement, evaluate and compare the quality of matching methods in quasi-experiment design.
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HANDS-ON EXERCISES
Build your Python workflow to make causal inferences in your research.
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CASUAL INFERENCE IN BIG DATA USING APACHE SPARK
Utilize Apache Spark to perform simple causal inference on big data in parallel.
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YOUR CASUAL
INFERENCE
TOOLKIT