  Understanding students' misconceptions is important for effective teaching
and assessment. However, discovering such misconceptions manually can be
time-consuming and laborious. Automated misconception discovery can address
these challenges by highlighting patterns in student data, which domain experts
can then inspect to identify misconceptions. In this work, we present a novel
method for the semi-automated discovery of problem-specific misconceptions from
students' program code in computing courses, using a state-of-the-art code
classification model. We trained the model on a block-based programming dataset
and used the learned embedding to cluster incorrect student submissions. We
found these clusters correspond to specific misconceptions about the problem
and would not have been easily discovered with existing approaches. We also
discuss potential applications of our approach and how these misconceptions
inform domain-specific insights into students' learning processes.
