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.
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.
Learn about how to make causal inferences in the context of educational research with big data.
Implement, evaluate and compare the quality of matching methods in quasi-experiment design.
Build your Python workflow to make causal inferences in your research.
Utilize Apache Spark to perform simple causal inference on big data in parallel.
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.
