Predicting Dropout Students

This repository contains web guides, R code, and sample data to help education data analysts consider machine learning and predictive modeling techniques when developing early warning indictors for likely dropout students. This material can also be accessed on the OpenSDP website at opensdp.github.io/analysis.

The code in this repository is based on code from the "Analyze" section of the Strategic Data Project Toolkit for Effective Data Use.

This repository is organized in the following folder structure:

• code contains the R code of the analysis (and renders the guide)
• R contains R functions necessary to complete the analysis
• data contains data necessary to complete the analysis
• img contains images to format the guides
• docs contains style assets to format the guides
• man contains the manuals and descriptive files of the data

These materials were originally authored by Dashiell Young-Saver and Jared Knowles.

OpenSDP is an online, public repository of analytic code, tools, and training intended to foster collaboration among education analysts and researchers in order to accelerate the improvement of our school systems. The community is hosted by the Strategic Data Project, an initiative of the Center for Education Policy Research at Harvard University. We welcome contributions and feedback.