Much research in Intelligent Tutoring Systems has explored
how to provide on-demand hints, how they should be used, and what
effect they have on student learning and performance. Most of this work
relies on hints created by experts and assumes that all help provided by
the tutor is correct and of high quality. However, hints may not all be
of equal value, especially in open-ended problem solving domains, where
context is important. This work argues that hint quality, especially when
using data-driven hint generation techniques, is inherently uncertain. We
investigate the impact of hint quality on students’ help-seeking behavior
in an open-ended programming environment with on-demand hints. Our
results suggest that the quality of the first few hints on an assignment is
positively associated with future hint use on the same assignment. Initial
hint quality also correlates with possible help abuse. These results have
important implications for hint design and generation.