In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while the latter is typically characterized by teacher-directed scenarios. Recent studies in the field of intelligent education have leveraged deep temporal models to trace the learning process, capturing the dynamics of students' knowledge states, and have achieved remarkable performance. However, existing approaches have primarily focused on modeling the independent learning process, with the group learning paradigm receiving less attention. Moreover, the reciprocal effect between the two learning processes, especially their combined potential to foster holistic student development, remains inadequately explored. To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes. Specifically, we first introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions across various time frames. Subsequently, we employ reciprocal enhanced learning modeling to fully exploit the comprehensive and complementary information between the two behaviors. Furthermore, we design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism. It is used to delve into the dynamic influence of individual and group interactions throughout the learning processes. Conclusively, we introduce a bias-aware contrastive learning module to bolster the stability of the model's training. Extensive experiments on four real-world educational datasets clearly demonstrate the effectiveness of the proposed RIGL model.

翻译：在教育领域，独立学习与群体学习均被视为最经典的学习范式。前者允许学习者自主规划学习进程，而后者通常以教师引导为特征。智能教育领域的最新研究利用深度时序模型追踪学习过程，捕捉学生知识状态的动态变化，并取得了显著成效。然而，现有方法主要集中于对独立学习过程进行建模，对群体学习范式的关注相对不足。此外，两种学习过程之间的互惠效应，尤其是它们共同促进学生全面发展的潜力，尚未得到充分探索。为此，本文提出RIGL——一种统一的互惠模型，旨在从独立学习与群体学习过程中追踪个体及群体层面的知识状态。具体而言，我们首先引入时间感知的互惠嵌入模块，以同时建模不同时间尺度下学生与群体的答题交互。随后，采用互惠增强学习建模方法，充分挖掘两种行为间全面且互补的信息。进一步地，我们设计了一种关系引导的时序注意力网络，该网络结合动态图建模与时序自注意力机制，用于深入探究学习过程中个体与群体交互的动态影响。最后，我们引入偏置感知的对比学习模块以增强模型训练的稳定性。在四个真实教育数据集上的大量实验充分证明了所提RIGL模型的有效性。