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Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing

机译：贝叶斯网络中的知识跟踪个人化建模

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The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade. Surprisingly, models currently in use do not allow for individual learning rates nor individualized estimates of student initial knowledge. Corbett and Anderson, in their original articles, were interested in trying to add individualization to their model which they accomplished but with mixed results. Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data. In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously, in a single step. With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter. We explore three difference strategies for setting the initial individualized knowledge parameters and report that the best strategy is one in which information from multiple skills is used to inform each student's prior. Using this strategy we achieved lower prediction error in 33 of the 42 problem sets evaluated. The implication of this work is the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill. Adaptation of instruction based on individualized knowledge and learning speed is discussed as well as open research questions facing those that wish to exploit student and skill information in their user models.
机译：智能补习系统领域一直使用由Corbett和Anderson（1995）推广的众所周知的知识追踪模型来追踪学生的知识已有十多年了。出人意料的是，当前使用的模型不允许个人学习率或学生初始知识的个性化估计。 Corbett和Anderson在他们的原始文章中对尝试将个性化添加到他们的模型中很感兴趣，他们完成了但结果却参差不齐。自从他们最初的工作以来，该领域在拟合数据中的知识跟踪模型的个性化方面尚未取得重大进展。在这项工作中，我们介绍了一种优雅的方法，可以在单个步骤中完全在贝叶斯网络框架内完全解决个性化问题以及特定于技能的参数，从而解决个性化问题。通过这种新的个性化技术，我们可以通过个性化初始知识参数来显示对现实世界数据预测的可靠改进。我们探索了三种用于设置初始个性化知识参数的差异策略，并报告说最好的策略是将多种技能的信息用于告知每个学生的先验知识。使用这种策略，我们在评估的42个问题集中的33个问题集中实现了较低的预测误差。这项工作的含义是能够增强现有的智能辅导系统，以更准确地估计学生何时掌握了技能。讨论了基于个性化知识和学习速度的教学适应性，以及那些希望在其用户模型中利用学生和技能信息的人面临的开放性研究问题。

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