In MOOCs for programming, Automated Testing and Feedback (ATF) systems are frequently integrated, providing learners with immediate feedback on code assignments. The analysis of the large amounts of trace data collected by these systems may provide insights into learners' patterns of utilizing the automated feedback, which is crucial for the design of effective tools and maximizing their potential to promote learning. However, data-driven research on the impact of ATF on learning is scarce, especially in the context of MOOCs. In the current study, we combine a theoretical framework of feedback with educational data mining methods to investigate the effect of feedback characteristics on learning behavior in a MOOC. Sequence pattern analysis is implemented to explore and visualize the actions taken by learners in response to feedback which composed of cognitive, meta-cognitive, and motivational elements. We applied our research approach in an empirical design which consists of five cohorts (total over 2200 learners) utilizing different versions of ATF. The findings suggest that learners tend to adopt learning strategies in response to feedback and exhibit a preference for utilizing example solutions, while still coping with the challenge of solving the assignments independently. The impact of feedback function, content and structure is discussed in light of a detailed view of the differences as well as common trends in learning paths. Allowing for fine-grained insights, we found our research approach contributes to a more comprehensive understanding of the effect of automated feedback characteristics in MOOCs for programming. [For the complete proceedings, see ED630829.]