Enhancing the automatic generation of hints with expert seeding
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
A comparison of two approaches for hint generation in programming tutors (abstract only)
Proceedings of the 45th ACM technical symposium on Computer science education
Experimental Evaluation of Automatic Hint Generation for a Logic Tutor
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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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.