  Learners regularly abandon online coding tutorials when they get bored or
frustrated, but there are few techniques for anticipating this abandonment to
intervene. In this paper, we examine the feasibility of predicting abandonment
with machine-learned classifiers. Using interaction logs from an online
programming game, we extracted a collection of features that are potentially
related to learner abandonment and engagement, then developed classifiers for
each level. Across the first five levels of the game, our classifiers
successfully predicted 61% to 76% of learners who did not complete the next
level, achieving an average AUC of 0.68. In these classifiers, features
negatively associated with abandonment included account activation and
help-seeking behaviors, whereas features positively associated with abandonment
included features indicating difficulty and disengagement. These findings
highlight the feasibility of providing timely intervention to learners likely
to quit.
