T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation

[2506.12075] T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2506.12075 (cs) [Submitted on 6 Jun 2025 ( v1 ), last revised 13 May 2026 (this version, v2)] Title: T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation Authors: Nirmal Gelal , Chloe Snow , Ambyr Rios , Kathleen M. Jagodnik , Hande Küçük McGinty View PDF HTML (experimental) Abstract: 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 this https URL . Comments: Under Review Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2506.12075 [cs.IR] (or arXiv:2506.12075v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2506.12075 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Data Mining and Knowledge Discovery (2026) Related DOI : https://doi.org/10.1007/s10618-026-01228-5 Focus to learn more DOI(s) linking to related resources Submission history From: Nirmal Gelal [ view email ] [v1] Fri, 6 Jun 2025 17:20:02 UTC (957 KB) [v2] Wed, 13 May 2026 14:53:40 UTC (971 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2025-06 Change to browse by: cs cs.AI References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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