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

OBJECTIVES

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

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.

OUR AMAZING TEAM

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

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