Knowledge Tracing (KT), namely tracking the knowledge conditions of each student across time, has always been challenging due to the latent and time-varying characteristics of knowledge states. Traditional psychometrical frameworks lack the ability to extract rich representations of exercises or examinees. Deep learning KT models have shown comparable performances, but uninterpretable model parameters have limited their applications. Furthermore, existing frameworks usually cannot handle temporal factors appropriately, as most of them simply apply stochastic processes to simulate fluctuations on knowledge states over time. In this paper, we propose a new framework named Time-and-Concept Enhanced Deep Multidimensional Item Response Theory (TC-MIRT) that integrates the parameters of a Multidimensional Item Response Theory into an improved recurrent neural network. Specifically, two enhanced components are constructed to empower our model with the ability to perform trend forecasting and to generate explainable parameters in each specific domain of knowledge. Experiments implemented on two real-world datasets show that our framework outperforms state-of-the-art KT approaches on performance prediction tasks. Moreover, extensive case analyses also demonstrate that the interpretable parameters of TC-MIRT can be used to evaluate the strengths and weaknesses of students.

知识跟踪（KT），即跨时间跟踪每个学生的知识状况，由于知识状态的潜伏性和时变特性，一直很具有挑战性。传统的心理测验框架缺乏提取练习或考生丰富表现形式的能力。深度学习KT模型已显示出可比的性能，但无法解释的模型参数限制了它们的应用。此外，现有的框架通常不能适当地处理时间因素，因为它们中的大多数只是简单地应用随机过程来模拟知识状态随时间的波动。在本文中，我们提出了一个名为“时间和概念增强型深度多维项目响应理论”（TC-MIRT）的新框架，该框架将多维项目响应理论的参数集成到了改进的递归神经网络中。具体来说，构建了两个增强的组件，以使我们的模型具有执行功能趋势预测，并在每个特定的知识领域中生成可解释的参数。在两个实际数据集上进行的实验表明，我们的框架在性能预测任务上的性能优于最新的KT方法。此外，大量案例分析还表明，TC-MIRT的可解释参数可用于评估学生的优缺点。