  Understanding why students stopout will help in understanding how students
learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we
build accurate predictive models of MOOC student stopout. We document a
scalable, stopout prediction methodology, end to end, from raw source data to
model analysis. We attempted to predict stopout for the Fall 2012 offering of
6.002x. This involved the meticulous and crowd-sourced engineering of over 25
predictive features extracted for thousands of students, the creation of
temporal and non-temporal data representations for use in predictive modeling,
the derivation of over 10 thousand models with a variety of state-of-the-art
machine learning techniques and the analysis of feature importance by examining
over 70000 models. We found that stop out prediction is a tractable problem.
Our models achieved an AUC (receiver operating characteristic
area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one
week in advance. Even with more difficult prediction problems, such as
predicting stop out at the end of the course with only one weeks' data, the
models attained AUCs of 0.7.
