  We consider the problem of assessing the changing performance levels of
individual students as they go through online courses. This student performance
(SP) modeling problem is a critical step for building adaptive online teaching
systems. Specifically, we conduct a study of how to utilize various types and
large amounts of student log data to train accurate machine learning (ML)
models that predict the performance of future students. This study is the first
to use four very large sets of student data made available recently from four
distinct intelligent tutoring systems. Our results include a new ML approach
that defines a new state of the art for logistic regression based SP modeling,
improving over earlier methods in several ways: First, we achieve improved
accuracy by introducing new features that can be easily computed from
conventional question-response logs (e.g., the pattern in the student 's most
recent answers). Second, we take advantage of features of the student history
that go beyond question-response pairs (e.g., features such as which video
segments the student watched, or skipped) as well as information about
prerequisite structure in the curriculum. Third, we train multiple specialized
SP models for different aspects of the curriculum (e.g., specializing in early
versus later segments of the student history), then combine these specialized
models to create a group prediction of the SP. Taken together, these
innovations yield an average AUC score across these four datasets of 0.808
compared to the previous best logistic regression approach score of 0.767, and
also outperforming state-of-the-art deep neural net approaches. Importantly, we
observe consistent improvements from each of our three methodological
innovations, in each dataset, suggesting that our methods are of general
utility and likely to produce improvements for other online tutoring systems as
well.
