Can Automated Feedback Turn Students into Happy Prologians?

Providing valuable and personalized feedback is essential for effective learning, but delivering it promptly can be challenging in large-scale courses. Recent research has explored automated feedback mechanisms across various programming languages and paradigms, including logic programming. In this work, we present a student survey were we evaluate the perceived usefulness of different feedback types and identified which are most valued. Our results indicate that students found all implemented feedback types helpful, with automatic testing ranked as the most useful. We also introduce a dataset comprising 7201 correct and incorrect Prolog submissions, along with 200 manually annotated programs labeled with bug types and corresponding corrections. Finally, we explore student preferences for which types of feedback they would most like to see implemented in the future.