Knowledge interaction enhanced sequential modeling for interpretable learner knowledge diagnosis in intelligent tutoring systems
Knowledge interaction enhanced sequential modeling for interpretable learner knowledge diagnosis in intelligent tutoring systems
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DOI：
10.1016/j.neucom.2022.02.080
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发表时间：
2022-03
期刊：
影响因子：
6
通讯作者：
Wenbin Gan;Yuan Sun;Yi Sun
中科院分区：
文献类型：
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作者：
Wenbin Gan;Yuan Sun;Yi Sun
在智能辅导系统、海量开放在线课程等在线学习系统中，为学习者提供个性化辅导服务的基本任务之一就是学习者知识诊断（LKD）。 LKD 通过对学习者的学习表现进行建模来获取学习者对技能的知识熟练程度。学习者的知识建构过程不是静态的，而是随着时间的推移而演变的；因此，必须动态跟踪学习者知识熟练程度的演变。此外，考虑到大量学习者对在线学习系统的广泛使用，LKD任务还需要满足大规模评估和可解释性的要求，以解释诊断结果。现有模型要么是针对静态场景设计的，要么很难解释学习者表现和知识熟练程度之间的因果关系以及项目特征。为了解决这些问题，我们在此提出了一种新颖的模型，称为知识交互增强动态 LKD（KIEDLKD），以开发学习者的表现，从而动态诊断和跟踪每个学习者在练习活动期间知识熟练程度的演变。我们首先提出了一个动态LKD框架，通过统一键值记忆网络的记忆能力强度来增强学习者表现建模过程中知识状态的表示和项目反应理论（IRT）的可解释性，以根据知识熟练程度和项目特征（即项目难度和辨别力）来解释学习者的表现。在此框架中，我们随着时间的推移诊断和跟踪每个学习者对每个知识概念（KC）的知识熟练程度，并使用键值存储网络将其存储到辅助存储器中。我们使用另一个神经网络进一步推断他们的一般熟练程度和基于 IRT 的项目特征。此外，我们提出了 KC 之间的知识交互概念，并将其合并到 LKD 过程中，以进一步利用锻炼序列中的长期依赖性，从而设计出 KIEDLKD 模型。我们还将基于每个学习者的锻炼历史的面向学习者的认知项目难度纳入我们的模型中，以自适应地建模项目难度。基于这些因素，我们的KIEDLKD模型不仅可以多粒度地输出学习者的知识熟练程度，还可以输出项目特征，从而可以根据学习者当前的知识状态和项目特征来解释学习者的表现。我们从六个角度对五个真实数据集进行了广泛的实验来测试我们的模型。学习器性能预测的结果证明了我们的模型在 LKD 任务上的优越性。它还可以自动发现每对潜在 KC 之间的潜在交互，以及每个练习的潜在概念。消融研究验证了模型中每个组件的贡献。此外，它可以以多粒度的方式描述学习者知识熟练程度的演变，并为技能领域分析提供附加信息，从而使我们的模型具有可解释性。
One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems, such as intelligent tutoring systems and massive open online courses, is the learner knowledge diagnosis (LKD). LKD obtains the learner knowledge proficiency on skills by modeling their learning performance. Learners’ knowledge construction process is not static, but evolves overtime; hence, the evolution of learners’ knowledge proficiency must be dynamically traced. Moreover, considering the wide usage of online learning systems by large numbers of learners, the LKD task also needs to meet the requirements of large-scale assessment and interpretability to explain the diagnosed results. The existing models are either designed for static scenarios or find it difficult to explain the causality between learner performance and knowledge proficiency, as well as the item characteristics. To solve these issues, we propose herein a novel model, called theknowledge interaction-enhanced dynamic LKD(KIEDLKD), to develop learner performance, and hence, dynamically diagnose and trace the evolution of each learner’s knowledge proficiency during the exercising activities. We first propose a dynamic LKD framework by unifying the strength of the memory capacity of the key-value memory network to enhance the representation of the knowledge state during learner performance modeling and the interpretability of the Item Response Theory (IRT) to explain the learner performance in terms of knowledge proficiency and item characteristics (i.e., item difficulty and discrimination). In this framework, we diagnose and trace each learner’s knowledge proficiency on each knowledge concept (KC) over time and store them into an auxiliary memory using the key-value memory network. We further infer their general proficiencies and the IRT-based item characteristics using another neural network. Moreover, we propose the knowledge interaction concept among KCs and incorporate it into the LKD procedure to further exploit the long-term dependencies in the exercising sequences, thereby devising the KIEDLKD model. We also incorporate the learner-oriented cognitive item difficulty into our model, based on each learner’s exercising history, to adaptively model the item difficulty. Based on these factors, our KIEDLKD model can not only output the learners’ knowledge proficiency in a multi-granularity manner but also output the item characteristics, making it possible to interpret the learner performances in terms of their current knowledge states and item characteristics. Extensive experiments are conducted from six perspectives on five real-world datasets to test our model.The results of learner performance prediction demonstrate the superiority of our model on the LKD task. It can also automatically discover the underlying interaction between each pair of latent KCs, and the underlying concepts for each exercise. The ablation study verifies the contributions of each component in our model. Moreover, it can depict the evolution of learner knowledge proficiency in a multi-granularity manner and provide additional information for skill domain analysis, which enables the interpretability of our model.