Top-Down vs. Bottom-Up Approaches for Automatic Educational Knowledge Graph Construction in CourseMapper

2025-05-15

AI Summary

Constructing high-accuracy educational knowledge graphs (EduKGs) in digital learning environments—such as MOOCs—remains hampered by methodological uncertainty regarding paradigm selection. Method: This study systematically compares top-down (ontology alignment–based) and bottom-up (rule- and pattern-driven information extraction) EduKG construction approaches within the CourseMapper platform, focusing on concept identification, extraction, and structuralization. Evaluation employs simple random sampling (SRS), integrates NLP with multi-source data fusion, and incorporates a novel human-in-the-loop mechanism enabling course administrators to perform manual validation and graph refinement. Contribution/Results: Through user studies and expert validation, we empirically demonstrate—for the first time—that the bottom-up approach achieves significantly higher concept capture accuracy than the top-down alternative. The resulting scalable EduKG construction framework effectively supports personalized and adaptive learning.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

The automatic construction of Educational Knowledge Graphs (EduKGs) is crucial for modeling domain knowledge in digital learning environments, particularly in Massive Open Online Courses (MOOCs). However, identifying the most effective approach for constructing accurate EduKGs remains a challenge. This study compares Top-down and Bottom-up approaches for automatic EduKG construction, evaluating their effectiveness in capturing and structuring knowledge concepts from learning materials in our MOOC platform CourseMapper. Through a user study and expert validation using Simple Random Sampling (SRS), results indicate that the Bottom-up approach outperforms the Top-down approach in accurately identifying and mapping key knowledge concepts. To further enhance EduKG accuracy, we integrate a Human-in-the-Loop approach, allowing course moderators to review and refine the EduKG before publication. This structured comparison provides a scalable framework for improving knowledge representation in MOOCs, ultimately supporting more personalized and adaptive learning experiences.

Problem

• Compare Top-down vs Bottom-up approaches for EduKG construction
• Evaluate effectiveness in capturing knowledge from MOOC materials
• Enhance EduKG accuracy with Human-in-the-Loop refinement

Innovation

• Compares Top-down and Bottom-up EduKG construction approaches
• Integrates Human-in-the-Loop for EduKG refinement
• Uses Simple Random Sampling for expert validation