PST: Measuring Skill Proficiency in Programming Exercise Process via Programming Skill Tracing

Ruixin Li, Yu Yin, Le Dai, Shuanghong Shen, Xin Lin, Yu Su, Enhong Chen

Abstract: Programming has become an important skill for individuals nowadays. For the demand to improve personal programming skill, tracking programming skill proficiency is getting more and more important. However, few researchers pay attention to measuring the programming skill of learners. Most of existing studies on learner capability portrait only made use of the exercise results, while the rich behavioral information contained in programming exercise process remains unused. Therefore, we propose a model that measures skill proficiency in programming exercise process named Programming Skill Tracing (PST). We designed Code Information Graph (CIG) to represent the feature of learners' solution code, and Code Tracing Graph (CTG) to measure the changes between the adjacent submissions. Furthermore, we divided programming skill into programming knowledge and coding ability to get more fine-grained assessment. Finally, we conducted various experiments to verify the effectiveness and interpretability of our PST model.