Knowledge tracing (KT) supports personalized learning by modeling how students' knowledge states evolve over time. However, most KT models emphasize mastery of discrete knowledge components, limiting their ability to characterize broader literacy development. We reframe the task as Literacy Tracing (LT), which models the growth of higher-order cognitive abilities and literacy from learners' interaction sequences, and we instantiate this paradigm with a Transformer-based model, TLSQKT (Transformer for Learning Sequences with Question-Aware Knowledge Tracing). TLSQKT employs a dual-channel design that jointly encodes student responses and item semantics, while question-aware interaction and self-attention capture long-range dependencies in learners' evolving states. Experiments on three real-world datasets - one public benchmark, one private knowledge-component dataset, and one private literacy dataset - show that TLSQKT consistently outperforms strong KT baselines on literacy-oriented metrics and reveals interpretable developmental trajectories of learners' literacy. Transfer experiments further indicate that knowledge-tracing signals can be leveraged for literacy tracing, offering a practical route when dedicated literacy labels are limited. These findings position literacy tracing as a scalable component of intelligent educational systems and lay the groundwork for literacy evaluation in future large-scale educational models.

翻译：知识追踪（KT）通过建模学生知识状态随时间演变的过程，以支持个性化学习。然而，大多数KT模型侧重于离散知识点的掌握程度，限制了其表征更广泛素养发展的能力。我们将该任务重新定义为素养追踪（LT），旨在从学习者的交互序列中建模高阶认知能力与素养的发展，并基于Transformer模型TLSQKT（面向学习序列的问题感知知识追踪Transformer）实例化这一范式。TLSQKT采用双通道设计，联合编码学生作答与题目语义，同时通过问题感知交互与自注意力机制捕捉学习者动态状态中的长程依赖关系。在三个真实数据集——一个公共基准数据集、一个私有知识点数据集和一个私有素养数据集——上的实验表明，TLSQKT在素养导向的评估指标上持续优于主流KT基线模型，并能揭示学习者素养发展的可解释轨迹。迁移实验进一步表明，知识追踪信号可被用于素养追踪，这为专用素养标签有限的情况提供了实用路径。这些发现将素养追踪定位为智能教育系统的可扩展组件，并为未来大规模教育模型中的素养评估奠定了基础。