A Comparison of the Quality of Data-driven Programming Hint Generation Algorithms


In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different hint generation approaches. In this work, we present the QualityScore procedure, a novel method for automatically evaluating and comparing the quality of next-step programming hints. We first demonstrate that the automated QualityScore ratings agree with experts’ manual ratings. We then use the QualityScore procedure to compare the quality of 6 data-driven, next-step hint generation algorithms using two distinct programming datasets in two different programming languages. Our results show that there are large and significant differences between the quality of the 6 algorithms and that these differences are relatively consistent across datasets and problems. We also identify situations where the six algorithms struggle to produce high-quality hints, and we suggest ways that future work might address these gaps in quality hint coverage.

International Journal of Artificial Intelligence in Education