Machine Learning-Based Automated Grading and Feedback Tools for Programming: A Meta-Analysis

Research into automated grading has increased as Computer Science courses grow. Dynamic and static approaches are typically used to implement these graders, the most common implementation being unit testing to grade correctness. This paper expands upon an ongoing systematic literature review to provide an in-depth analysis of how machine learning (ML) has been used to grade and give feedback on programming assignments. We conducted a backward snowball search using the ML papers from an ongoing systematic review and selected 27 papers that met our inclusion criteria. After selecting our papers, we analysed the skills graded, the preprocessing steps, the ML implementation, and the models' evaluations.