Enhancing a student productivity model for adaptive problem-solving assistance.
Enhancing a student productivity model for adaptive problem-solving assistance.
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DOI：
10.1007/s11257-022-09338-7
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发表时间：
2023
影响因子：
3.6
通讯作者：
Barnes T
中科院分区：
文献类型：
--
作者：
Maniktala M;Chi M;Barnes T
智能教学系统的研究一直在探索数据驱动的方法来提供有效的自适应辅助。虽然已经做了很多工作，以提供适应性的援助，当学生寻求帮助，他们可能不会寻求最佳的帮助。这导致了越来越多的兴趣，主动适应援助，导师提供未经请求的援助后，预测的斗争或非生产力。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。解决这个难题在开放式领域中尤其具有挑战性，在开放式领域中可以有几种方法来解决问题。研究人员已经探索了确定何时主动帮助学生的方法，但这些方法很少考虑到之前的暗示使用情况。在本文中，我们提出了一种新的数据驱动的方法，将学生的提示使用预测他们的需要帮助。我们探讨其影响，在一个智能导师，处理开放式和结构良好的域的逻辑证明。我们提出了一个对照研究调查的影响，自适应提示政策的基础上预测的帮助需要，将学生的提示使用。我们的经验证据表明，这样的政策可以节省学生大量的时间在培训和导致改善后测结果相比，没有积极的干预措施的控制。我们还表明，将学生的提示使用显着提高自适应提示政策的有效性，在预测学生的HelpNeed，从而减少培训的非生产力，减少可能的帮助避免，并增加可能的帮助适当性（更高的机会，收到帮助时，它可能需要）。最后，我们对可以从这种方法中受益的领域以及采用的要求提出了建议。
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.
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DOI：
10.1007/s40593-016-0131-y
发表时间：
2017-06-01
影响因子：
4.9
作者：
Fratamico, Lauren;Conati, Cristina;Roll, Ido
通讯作者：
Roll, Ido
影响因子：
3.6
作者：
Feng, Mingyu;Heffernan, Neil;Koedinger, Kenneth
通讯作者：
Koedinger, Kenneth
影响因子：
3.6
作者：
CORBETT, AT;ANDERSON, JR
通讯作者：
ANDERSON, JR
影响因子：
3.3
作者：
Kinnebrew, John S.;Segedy, James R.;Biswas, Gautam
通讯作者：
Biswas, Gautam
影响因子：
4.9
作者：
Azevedo, R;Cromley, JG
通讯作者：
Cromley, JG