  In programming education, providing manual feedback is essential but
labour-intensive, posing challenges in consistency and timeliness. We introduce
ECHO, a machine learning method to automate the reuse of feedback in
educational code reviews by analysing patterns in abstract syntax trees. This
study investigates two primary questions: whether ECHO can predict feedback
annotations to specific lines of student code based on previously added
annotations by human reviewers (RQ1), and whether its training and prediction
speeds are suitable for using ECHO for real-time feedback during live code
reviews by human reviewers (RQ2). Our results, based on annotations from both
automated linting tools and human reviewers, show that ECHO can accurately and
quickly predict appropriate feedback annotations. Its efficiency in processing
and its flexibility in adapting to feedback patterns can significantly reduce
the time and effort required for manual feedback provisioning in educational
settings.
