Automatic feedback and resubmissions as learning aid

Feedback based on automatic assessment of students' solutions is an important aid for students' learning process in self-study and distance learning. Most automatic assessment systems allow students to revise their solutions after getting the feedback and resubmit their work to be able to complete the exercise. In this paper, we analyze the effect of re submission in detail in the context of automatically assessed algorithm simulation exercises. In the target system TRAKLA2, students can revise their answers as many times as they wish, but each trial requires to restart the exercise with new random data. We present statistical results from a course with 600 students and show that our method that combines resubmissions and exercises with randomized initial data has a positive effect on learning results.