Instructors and students alike are often focused on the grade in programming assignments as a key measure of how well a student is mastering the material and whether a student is struggling. This can be, however, misleading. Especially when students have access to auto-graders, their grade may be heavily skewed. In this paper, we analyze student assignment submission data collected from a functional programming course taught at X university incorporating a wide range of features. In addition to the grade, we consider activity time data, time spent, and the number of static errors. This allows us to identify four clusters of students: “Quick learning”, “Hardworking”, “Satisficing”, and “Struggling” through cluster algorithms. We then analyze how work habits, working duration, the range of errors, and the ability to fix errors impacts different clusters of students. This structured analysis provides valuable insights for instructors to actively help different types of students and emphasize different aspects in their overall course design. It also provides insights for students themselves to understand which aspects they still struggle with and allows them to seek clarifications and adjust their work habits.