The document proposes using a knowledge graph to provide explainable recommendations of external content from sources like Wikipedia to supplement an electronic textbook. It describes challenges like resource quality and the need for expert-driven analysis. The approach creates a knowledge graph connecting the textbook, student models, and Wikipedia articles. Recommendations are generated based on a student's partial or missing knowledge according to the graph. An evaluation found the knowledge graph approach improved recommendation quality and better predicted students' knowledge needs compared to a baseline.
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
Using Knowledge Graph for Explainable Recommendation in E-Textbooks
1. Using Knowledge Graph for Explainable
Recommendation of External Content in Electronic
Textbooks
Behnam Rahdari, Peter Brusilovsky, Khushboo Thaker and Jordan Barria-
Pineda
University of Pittsburgh, PA, USA
3. Close Corpus vs Open Corpus
Section(n) Section(m)
Section(n)
Section(m)
Open Corpus Close Corpus
External Resources Textbook itself
Challenging because of:
• Quality of resources
• Expert-driven knowledge analysis
02/17
12. Assessment – Data & Baseline
• Real Classroom Data
• Information Retrieval
• Semester-Long Logs
• E-Textbook:
• 43 Sections
• 75 Questions
• 22 Students (9494 interactions)
• Baseline:
• Recommendations based on the content of Sections/Questions
11/17
13. Assessment - Ranking Quality Measurement
Discounted Cumulative Gain
• Takes into account both:
• the relevance score
• the order of items in the recommendations list
• Uniform Relevance Score in both systems (Experimental and Baseline)
12/17
14. Assessment - Overall Expected Knowledge Value
• Comparison of Average DCG among sections and questions
• Higher DCG in all cases
• Average of 23.29% higher among all sections
• Average of 30.27% higher among all questions
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δ Baseline Proposed
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δ Baseline Proposed
13/17
15. Assessment - Overall Expected Knowledge Value
• Difference between DCG among:
• Left: Students/Sections
• Right: Students/Questions
• Different Students require different Recommendations
14/17
16. Assessment - Predicting User's Knowledge Requirements
• Jumping-Back behavior
• In average 17.27% of all students’ Interactions
• How two approaches predict the concepts in target sections/questions
15/17
17. Summary & Future Works
• A novel approach :
• Generate personalized recommendations of external content for
online electronic textbooks.
• Combining Textbook Content and Student Model
• Predicts:
• Missing knowledge components
• Jumping-back behavior
• Future Works:
• Real-time recommendation during the semester
• Adding more factors: difficulty of learning concepts, forgetting factor, etc.
16/17