Immediate and personalized feedback on students’ programming assignments is important for improving their programming skills. However, it is challenging for instructors to give personalized feedback to every student since each program is written differently. To address this problem, the Automated Feedback Generation (AFG) technique has been proposed, which identifies faults from wrong program, generates patches, and provides feedback if pass validation. AFG relies on correct programs from students to find faults and generate patches. Therefore, having diverse correct programs is important for the performance of AFG. However, in small-scale programming courses or new online judge problems, might be a lack of diversity in correct programs. In this paper, we propose Mentored, a new AFG for students’ programming assignments through diversification. Mentored generates new structures of programs through various combinations of programs to generate modifications optimized for wrong programs while solving the problem of dependency on correct programs. Additionally, Mentored provides transparent feedback on the process of repairing wrong programs. We evaluate Mentored on real student programming assignments and compare it with state-of-the-art AFG approaches. Our dataset includes real university introductory programming assignments and online judge problems. Experimental results show that Mentored generates higher repair rates and more diverse program structures than other AFG approaches. Moreover, by providing a transparent sequence of repair processes, Mentored is expected to improve students’ programming skills and reduce instructors’ manual effort in feedback generation. These results indicate that Mentored can be a useful tool in programming education.