LEAP protocol applied to OULAD dataset demonstrates that enforcing temporal cutoffs reveals realistic performance curves in early LMS prediction, with gains around week 3 and clear inflation from assessment leakage.
Automated skill decomposition meets expert ontologies: Bridging the granularity gap with llms
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.
An LLM+BM25+graph pipeline tags learning resources to competencies with evidence spans, reaching 0.57 micro-F1 and 0.50 macro-F1 at fragment level on a 22-competency university dataset while outperforming baselines.
SkillGraph-Service builds a provenance-preserving knowledge graph from multiple competency frameworks and achieves nDCG@5 above 0.94 with sub-200 ms latency via KG-first hybrid retrieval and constrained LLM explanations.
citing papers explorer
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When Can We Trust Early Warnings? Leakage-Excluded Early Outcome Prediction from LMS Interaction Logs
LEAP protocol applied to OULAD dataset demonstrates that enforcing temporal cutoffs reveals realistic performance curves in early LMS prediction, with gains around week 3 and clear inflation from assessment leakage.
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From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Presents the CCAI ontology and SPARQL retrieval method to convert ephemeral Human-Generative AI prompt interactions into explicit, machine-readable collaboration traces, illustrated in a competency-profile software case study.
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From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints
An LLM+BM25+graph pipeline tags learning resources to competencies with evidence spans, reaching 0.57 micro-F1 and 0.50 macro-F1 at fragment level on a 22-competency university dataset while outperforming baselines.
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KG-First, LLM-Fallback: A Hybrid Microservice for Grounded Skill Search and Explanation
SkillGraph-Service builds a provenance-preserving knowledge graph from multiple competency frameworks and achieves nDCG@5 above 0.94 with sub-200 ms latency via KG-first hybrid retrieval and constrained LLM explanations.