  For teachers, automated tool support for debugging and assessing their
students' programming assignments is a great help in their everyday business.
For block-based programming languages which are commonly used to introduce
younger learners to programming, testing frameworks and other software analysis
tools exist, but require manual work such as writing test suites or formal
specifications. However, most of the teachers using languages like Scratch are
not trained for or experienced in this kind of task. Linters do not require
manual work but are limited to generic bugs and therefore miss potential
task-specific bugs in student solutions. In prior work, we proposed the use of
anomaly detection to find project-specific bugs in sets of student programming
assignments automatically, without any additional manual labour required from
the teachers' side. Evaluation on student solutions for typical programming
assignments showed that anomaly detection is a reliable way to locate bugs in a
data set of student programs. In this paper, we enhance our initial approach by
lowering the abstraction level. The results suggest that the lower abstraction
level can focus anomaly detection on the relevant parts of the programs.
