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Using Knowledge Graph for ExplainableRecommendation of External Content inElectronic Textbooks

Over the last 10 years, the world experienced a rapid increase in volume and diversity of digital learning resources. The abundance of digital resources could support a range of powerful educational scenarios, which were not available before. In this paper, we introduce a novel approach that combines fully automatic knowledge modeling, student modeling, and content recommendation approaches to recommend relevant Wikipedia articles for students working with online electronic textbooks. An assessment of our approach with real classroom data indicated several benefits of our approach over the baseline and revealed interesting patterns of students' behavior while using the system.

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Using Knowledge Graph for ExplainableRecommendation of External Content inElectronic Textbooks

  1. 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
  2. 2. History of Textbook Physical Textbooks Electronic Textbooks Online Reading Systems … Print/Paper Digital/Computer Internet/online • Evolution of Textbooks • Smart Learning • Feasibility > reliance of technology rather than only educators • Personalization > tailored to student’s need 01/17
  3. 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
  4. 4. Proposed Approach Recommendations Student Model Textbook Wikipedia Addressing the two Challenges: • Quality of resources • Using Wikipedia • Uniform and up-to-date • Reliable and popular • Expert-driven knowledge analysis • Fully automated process • Real-time recommendation • Utilizing the student model 03/17
  5. 5. Reading Mirror-Main Interface 1 2 3 1- Relevance Bar 2- Recommendations 3- Explanations 04/17
  6. 6. Reading Mirror- Explanations Intermediate Dialog Explanation Dialog 05/17
  7. 7. The Knowledge Graph - Overview 1 2 3 Student Model Wikipedia Articles 06/17
  8. 8. Knowledge Graph- Wikipedia Entities Main Category Sub- Category Article Article Sub- Category Article ... ... ... ... Top Category Subfields of computer science 1,141 categories 47,772 articles Title, Summary & Full-Text 07/17
  9. 9. Knowledge Graph – Textbook Entities Textbook Section Concept Concept Question Concept ... ... ... ... • Textbook Content • Section: • Includes: Sub-sections • Questions: • Connect to a Section • Concepts: • Extracted From the text Automatic Extraction + Manual Indexing by Experts 08/17
  10. 10. Knowledge Graph- Student Model Student read Section Section Answer Question ... ... ... ... • Input < Student Activity • Output > Student Performance • Comprehension Factor Analysis framework (CFM) • Reading behavior • Question Answering • Each Student Presented as a single Node in the graph • Knowledge: Connection to a Concept • What section/question • When 09/17
  11. 11. Recommendation Approach Obtained Knowledge Partial Knowledge Missing Knowledge Required Knowledge Useful Knowledge • Required Knowledge • For Section : • All Concepts in the Section • For Question : • All Concepts in Question + • All Concepts in Section • Real-Tile Calculation • Cypher Query Language • Relevancy Metric (concept & Articles) • Lucene Search 10/17
  12. 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. 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. 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 0 25 50 75 100 261 263 265 267 269 271 273 275 277 279 281 307 309 311 313 315 325 327 329 331 333 335 337 339 341 387 389 391 393 396 398 400 402 404 408 410 412 414 δ Baseline Proposed 0 25 50 75 100 1.1 1.4 4.2 4.4 4.6 5.2 6.1 6.3 8.1 8.3 8.5 8.7 9.2 12.4 13.2 13.4 13.6 14.2 14.4 14.6 16.2 16.4 δ Baseline Proposed 13/17
  15. 15. Assessment - Overall Expected Knowledge Value • Difference between DCG among: • Left: Students/Sections • Right: Students/Questions • Different Students require different Recommendations 14/17
  16. 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. 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

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