Date
Publisher
arXiv
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
Study at a glance
- Question: Can an automated recommendation system help high school English teachers select diverse and thematically aligned literature books?
- Setting and data: Not stated in the abstract
- Method: Development and computational evaluation of a book recommendation system using knowledge graphs and link prediction
Why it matters
Due to limited planning time and resources, high school English Literature teachers often struggle to curate diverse, thematically aligned literature text sets. Novice educators have a critical need for scaffolds to select diverse texts with pedagogical merits.
Key findings
- In high school English literature, a recommendation tool suggests it can significantly ease teachers' text selection burden, promoting more informed and inclusive curricular decisions.
- One computational model outperformed other methods across most ranking measures, while a hybrid approach offered balanced overall performance.
- Structuring recommendations around meaningful educational concepts and themes proved essential for building effective literature recommendation systems.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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Who is the user?
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Study design
