Program Representation for Automatic Hint Generation for a Data-Driven Novice Programming Tutor

Wei Jin, Tiffany Barnes, John Stamper, Michael John Eagle, Matthew W. Johnson, Lorrie Lehmann

We describe a new technique to represent, classify, and use programs written by novices as a base for automatic hint generation for programming tutors. The proposed linkage graph representation is used to record and reuse student work as a domain model, and we use an overlay comparison to compare in-progress work with complete solutions in a twist on the classic approach to hint generation. Hint annotation is a time consuming component of developing intelligent tutoring systems. Our approach uses educational data mining and machine learning techniques to automate the creation of a domain model and hints from student problem-solving data. We evaluate the approach with a sample of partial and complete, novice programs and show that our algorithms can be used to generate hints over 80 percent of the time. This promising rate shows that the approach has potential to be a source for automatically generated hints for novice programmers.

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_40.