Song Cheng, Qi Liu, Enhong Chen, Kai Zhang, Zhenya Huang, Yu Yin, Xiaoqing Huang, Yu Su
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In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. 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引用次数: 3
摘要
知识跟踪是在线教育系统的一项重要而基础的任务,它可以预测学生的知识状态,为个性化学习提供依据。不幸的是,现有的方法是特定于领域的,而真实的教育场景中有许多领域(如学科、学校),一些领域存在缺乏足够数据的问题。因此,如何利用其他领域的知识,以提高模型在目标领域的性能仍然是一个非常开放的问题。我们将这一问题称为知识跟踪的领域适应(Domain Adaptation for Knowledge Tracing, DAKT),其目的是将知识从源领域转移到目标领域进行知识跟踪。在本文中,我们提出了一种新的适应性方法,即适应性知识追踪(AdaptKT),该方法分为三个阶段来探索这一问题。具体来说,第一阶段是实例选择。给定两个领域的问题文本,我们训练一个自编码器从两个领域中选择和嵌入相似的实例。第二阶段是分配差异最小化。在获得所选择的实例及其语言表示后,我们训练了一个知识跟踪模型,并采用最大平均差异(MMD)来最小化领域特定知识状态分布之间的差异。第三阶段是输出层的微调。我们将第二阶段训练的模型的输出层替换为新的输出层,使知识跟踪模型的输出维数与目标领域中知识概念的数量相匹配。新的输出层被训练,而之前的其他参数被冻结。我们在两个大规模的真实数据集上进行了大量的实验,实验结果清楚地证明了AdaptKT解决DAKT问题的有效性。论文通过后,我们将在Github上公开代码。
AdaptKT: A Domain Adaptable Method for Knowledge Tracing
Knowledge tracing is a crucial and fundamental task in online education systems, which can predict students' knowledge state for personalized learning. Unfortunately, existing methods are domain-specific, whereas there are many domains (e.g., subjects, schools) in the real education scene and some domains suffer from the problem of lacking sufficient data. Therefore, how to exploit the knowledge in other domains, to improve the model's performance for target domain remains pretty much open. We term this problem as Domain Adaptation for Knowledge Tracing (DAKT), which aims to transfer knowledge from the source domain to the target one for knowledge tracing. In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. We will public the code on the Github after the acceptance of the paper.