Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help
Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help
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DOI：
10.5281/zenodo.4399683
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发表时间：
2020-10
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影响因子：
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通讯作者：
Mehak Maniktala;Christa Cody;Amy Isvik;Nicholas Lytle;Min Chi;T. Barnes
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文献类型：
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作者：
Mehak Maniktala;Christa Cody;Amy Isvik;Nicholas Lytle;Min Chi;T. Barnes
确定何时以及是否提供个性化支持是一个众所周知的挑战，称为援助困境。解决援助困境的一个核心问题是需要发现学生何时没有生产力，以便导师可以进行干预。这样的任务对于开放式领域尤其具有挑战性，即使是那些具有明确原则和目标的结构良好的领域。在本文中，我们提出了一套数据驱动的方法来分类，预测，并防止在结构良好的开放式逻辑领域的非生产性问题解决步骤。这种方法利用并扩展了提示工厂，这是一组利用先前学生解决方案尝试构建数据驱动智能导师的方法。我们提出了一个HelpNeed分类，使用以前的学生数据，以确定学生可能是非生产性的，需要帮助学习最佳的解决问题的策略。我们提出了一个对照研究，以确定自适应教学政策的影响，提供积极的提示，在开始的每一步的基础上，我们的HelpNeed预测的结果：生产与非生产性。我们的研究结果表明，在自适应条件下的学生表现出更好的训练行为，较低的帮助回避，和较高的帮助适当性（更高的机会，接受帮助时，它可能是需要的），使用HelpNeed分类器测量，当与控制相比。此外，结果表明，在培训期间收到基于HelpNeed预测的自适应提示的学生在后测中的表现显着优于对照组同龄人，前者在更短的时间内产生更短、更优化的解决方案。最后，我们建议如何将这些HelpNeed方法应用于其他结构良好的开放式领域。
Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. Such a task is particularly challenging for open-ended domains, even those that are well-structured with defined principles and goals. In this paper, we present a set of data-driven methods to classify, predict, and prevent unproductive problem-solving steps in the well-structured open-ended domain of logic. This approach leverages and extends the Hint Factory, a set of methods that leverages prior student solution attempts to build data-driven intelligent tutors. We present a HelpNeed classification, that uses prior student data to determine when students are likely to be unproductive and need help learning optimal problem-solving strategies. We present a controlled study to determine the impact of an Adaptive pedagogical policy that provides proactive hints at the start of each step based on the outcomes of our HelpNeed predictor: productive vs. unproductive. Our results show that the students in the Adaptive condition exhibited better training behaviors, with lower help avoidance, and higher help appropriateness (a higher chance of receiving help when it was likely to be needed), as measured using the HelpNeed classifier, when compared to the Control. Furthermore, the results show that the students who received Adaptive hints based on HelpNeed predictions during training significantly outperform their Control peers on the posttest, with the former producing shorter, more optimal solutions in less time. We conclude with suggestions on how these HelpNeed methods could be applied in other well-structured open-ended domains.