High school English Literature teachers often encounter barriers to assembling diverse, thematically aligned text sets due to limited planning time and pedagogical resources. To address this need, we present T-TExTS (Teaching Text Expansion for Teacher Scaffolding), a knowledge graph (KG)-based recommendation system that suggests literature texts based on pedagogical merit rather than surface-level metadata. We construct a domain-specific ontology using the Knowledge Acquisition and Representation Methodology (KNARM), instantiate it as a knowledge graph with separate Terminological Box (TBox) and Assertional Box (ABox) components, and evaluate four graph embedding strategies (DeepWalk, biased random walk, hybrid embedding, and Node2Vec) across three dataset configurations (98, 196, and 351 texts) and two relation-weighting schemes. The experimental results reveal that traversal-level expert weighting alone does not outperform algorithmic structural tuning: Node2Vec achieves the highest Area Under the Curve (AUC) at every dataset size (0.9642--0.9750) and the strongest ranking metrics (Hits@K, MRR, nDCG) at larger scales. Combining structural and pedagogical signals through embedding concatenation, however, preserves both interpretability and competitive ranking quality, with the hybrid model maintaining a high AUC across all scales (0.9122--0.9350) and remaining within a few percentage points of Node2Vec on every ranking metric. These findings highlight the value of ontology-driven knowledge graph embeddings for educational recommendation systems and demonstrate that T-TExTS can meaningfully ease the burden of English Literature text selection for secondary educators, supporting more informed and inclusive curricular decisions. The source code for T-TExTS is available at https://github.com/koncordantlab/TTExTS.

翻译：高中英语文学教师常因备课时间有限和教学资源不足，在构建多样化、主题一致的文本集时面临障碍。为应对这一需求，我们提出T-TExTS（面向教师支架的教学文本扩展）——一种基于知识图谱（KG）的推荐系统，该系统依据教学价值而非表层元数据推荐文学文本。我们采用知识获取与表征方法论（KNARM）构建领域特定本体，将其实例化为具有独立术语盒（TBox）与断言盒（ABox）组件的知识图谱，并在三种数据集配置（98篇、196篇、351篇文本）与两种关系权重方案下，评估了四种图嵌入策略（DeepWalk、偏置随机游走、混合嵌入及Node2Vec）。实验结果表明，仅凭遍历层面的专家权重并未超越算法结构调优：Node2Vec在所有数据集规模下均取得最高AUC值（0.9642-0.9750），并在大规模数据下获得最优排序指标（Hits@K、MRR、nDCG）。然而，通过嵌入拼接融合结构信号与教学信号，混合模型既保留了可解释性又保持了具有竞争力的排序质量，其AUC在全规模下保持高水平（0.9122-0.9350），且各项排序指标与Node2Vec的差距均在数个百分比以内。这些发现凸显了本体驱动型知识图谱嵌入在教育推荐系统中的价值，并证明T-TExTS能切实减轻中学英语文学教师的选文负担，支持更具包容性和信息化的课程决策。T-TExTS源代码已发布于https://github.com/koncordantlab/TTExTS。