Customizing Feedback for Introductory Programming Courses Using Semantic Clusters

摘要

The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress; however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesive-ness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation.