  The 2017 ASSISTments Data Mining competition aims to use data from a
longitudinal study for predicting a brand-new outcome of students which had
never been studied before by the educational data mining research community.
Specifically, it facilitates research in developing predictive models that
predict whether the first job of a student out of college belongs to a STEM
(the acronym for science, technology, engineering, and mathematics) field. This
is based on the student's learning history on the ASSISTments blended learning
platform in the form of extensive clickstream data gathered during the middle
school years. To tackle this challenge, we first estimate the expected
knowledge state of students with respect to different mathematical skills using
a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then
combine the features corresponding to the DKT/DKT+ expected knowledge state
with other features extracted directly from the student profile in the dataset
to train several machine learning models for the STEM/non-STEM job prediction.
Our experiments show that models trained with the combined features generally
perform better than the models trained with the student profile alone. Detailed
analysis of the student's knowledge state reveals that, when compared with
non-STEM students, STEM students generally show a higher mastery level and a
higher learning gain in mathematics.
