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

• URL: http://arxiv.org/abs/2506.12075v1
• Date: Fri, 06 Jun 2025 17:20:02 GMT
• 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, Hande Küçük McGinty,
• Abstract summary: This study addresses the critical need for a tool that provides scaffolds for novice educators in selecting literature texts.<n>We have developed a recommendation system, Teaching Text Expansion for Teacher Scaffolding (T-TExTS)<n>It suggests high school English Literature books based on pedagogical merits, genre, and thematic relevance using a knowledge graph.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The implementation of transformational pedagogy in secondary education classrooms requires a broad multiliteracy approach. Due to limited planning time and resources, high school English Literature teachers often struggle to curate diverse, thematically aligned literature text sets. This study addresses the critical need for a tool that provides scaffolds for novice educators in selecting literature texts that are diverse -- in terms of genre, theme, subtheme, and author -- yet similar in context and pedagogical merits. We have developed a recommendation system, Teaching Text Expansion for Teacher Scaffolding (T-TExTS), that suggests high school English Literature books based on pedagogical merits, genre, and thematic relevance using a knowledge graph. We constructed a domain-specific ontology using the KNowledge Acquisition and Representation Methodology (KNARM), transformed into a knowledge graph, which was then embedded using DeepWalk, biased random walk, and a hybrid of both approaches. The system was evaluated using link prediction and recommendation performance metrics, including Area Under the Curve (AUC), Mean Reciprocal Rank (MRR), Hits@K, and normalized Discounted Cumulative Gain (nDCG). DeepWalk outperformed in most ranking metrics, with the highest AUC (0.9431), whereas the hybrid model offered balanced performance. These findings demonstrate the importance of semantic, ontology-driven approaches in recommendation systems and suggest that T-TExTS can significantly ease the burden of English Literature text selection for high school educators, promoting more informed and inclusive curricular decisions. The source code for T-TExTS is available at: https://github.com/koncordantlab/TTExTS

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