Data Mining for Generating Hints in a Python Tutor

In this paper we present a tool where both past and current student data is used live to generate hints for students who are completing programming exercises during a national programming online tutorial and competition. These hints can be links to notes that are relevant to the problem detected and can include pre-emptive hints to prevent future mistakes. Data from the year 2008 was mined, using clustering, association rules and numerical analysis, to find common patterns affecting the learners’ performance that we could use as a basis for providing hints to the 2009 students. During its live operation in 2009, student data was mined each week to update the system as it was being used. The benefits of the hinting system were evaluated through a large-scale experiment with participants of the 2009 NCSS Challenge. We found that users who were provided with hints achieved higher average marks than those who were not and stayed engaged for longer with the site.