Programming Hints Generation based on Abstract Syntax Tree Retrieval

This paper presents research that Works in Progress (WIP). Small private online courses (SPOCs) have recently received extensive attention in computing education. In SPOCs, programming exercises are frequently included to train students’ programming skills. Abstract Syntax Tree Retrieval (ASTR) is a system that can help students solve Python problems by inferring the coding goals. However, the coding goal retrieved by ASTR gives students little information about what to do next. In response to this limitation, this work focuses on generating modification hints for students based on the coding goal. In addition, this paper reports on an effort to translate this idea over to Verilog-HDL programming problems. Without any programmed expert knowledge, the final results demonstrate that our system is generally accurate for 1 out of 2 submissions to give hints at a minimum. And for some favorable problems, it potentially performs much better. Furthermore, the results indicate that in the process of retrieval, weighted tree edit distance calculations resulted in improved accuracy over metric tree edit distance calculations.