A New Evaluation Metrics for Block-based Python']Python Code

The programming education for the students set off a global enthusiasm. The Block-Python programming tools can fill the gap from block-based programming to language coding. However, there is little work focus on the evaluation metrics for block-based python code. The past block-oriented evaluation metrics based on Computational Thinking (CT) were not language-oriented, and program volume was often ignored. In this paper, we propose a new evaluation metrics for block-based python code, which combines the CT dimensions and program volume. The experimental results show that the correlations between the proposed metrics and Halstead, McCabe complexity algorithms are both above 0.7, which is higher than Dr.Scratch.