Students’ Debugging Behavior Analysis in Game-Based Learning

Abstract

As programing became more and more important, people are taking a large amount of work to help students to learn programming skills effectively. This paper applies a programming learning game called May's Journey to fit 5 debugging types including syntax, logical, structure, reasoning, and undefined debugging errors into programming levels. Then we can find out the reason why students make mistakes, and which debugging type would cause the mistakes of other debugging types. And we have 6 findings, (1) This paper proposes a student debugging model to describe how students make debugging errors, which is used for further analysis on student debugging behaviors. (2) This paper proposes to use group mean and with-in group variance based on student debugging model, which finds out the common debugging errors and personal debugging errors. (3) This paper proposes to extract student debugging patterns using Random forest, which identifies student debugging behaviors, so that students who have the same debugging pattern can be trained together. (4) This paper also proposes to use student debugging model-based SVM to extract student performance patterns, which identifies student performance changing over programming levels in terms of a specific debugging type. (5) This paper proposes to apply mean decrease accuracy and mean decrease Gini to identify the effectiveness of debugging types; and (6) this paper proposes to use a classification-based LSTM algorithm to predict debugging errors, which improves the predication accuracy a lot. Experiments and results are also provided to prove that our methods are valid and better.