Research on intelligent tutoring systems has been exploring data-driven
methods to deliver effective adaptive assistance. While much work has been done
to provide adaptive assistance when students seek help, they may not seek help
optimally. This had led to the growing interest in proactive adaptive
assistance, where the tutor provides unsolicited assistance upon predictions of
struggle or unproductivity. Determining when and whether to provide
personalized support is a well-known challenge called the assistance dilemma.
Addressing this dilemma is particularly challenging in open-ended domains,
where there can be several ways to solve problems. Researchers have explored
methods to determine when to proactively help students, but few of these
methods have taken prior hint usage into account. In this paper, we present a
novel data-driven approach to incorporate students' hint usage in predicting
their need for help. We explore its impact in an intelligent tutor that deals
with the open-ended and well-structured domain of logic proofs. We present a
controlled study to investigate the impact of an adaptive hint policy based on
predictions of HelpNeed that incorporate students' hint usage. We show
empirical evidence to support that such a policy can save students a
significant amount of time in training, and lead to improved posttest results,
when compared to a control without proactive interventions. We also show that
incorporating students' hint usage significantly improves the adaptive hint
policy's efficacy in predicting students' HelpNeed, thereby reducing training
unproductivity, reducing possible help avoidance, and increasing possible help
appropriateness (a higher chance of receiving help when it was likely to be
needed). We conclude with suggestions on the domains that can benefit from this
approach as well as the requirements for adoption.

本研究提出了一种基于数据驱动的方法来预测学生需要帮助的概率，并探讨了在逻辑证明等深度学科中，将学生的暗示使用情况纳入预测模型的有效性，结果表明这种主动适应的策略可以显著提高学生的学习效率和提高授课的质量。