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Kowledge zoom michelle

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ICALT 2013

Published in: Data & Analytics
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Kowledge zoom michelle

  1. 1. KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model Peter Brusilovsky, Dhruba Baishya, Roya Hosseini, Julio Guerra, and MinEr Liang
  2. 2. KnowledgeZoom for Java: A Concept-Based Exam Study Tool with a Zoomable Open Student Model • Problems/Motivation • Our Proposed Approach • Related Work • The KnowledgeZoom Study Tool – The Knowledge Explorer – The Knowledge Maximizer • Evaluation • Summary and Future Work 2/19
  3. 3. Problems/Motivation • Students need special helps to prepare for their final exams within a short time – To review content of the whole semester quickly – To fill up the knowledge gaps precisely • But most of the existing personalized learning tools focus on regular semester-long studies – Fine-grained concept-based guidance – Coarse-grained topic-based guidance – Social guidance 3/19
  4. 4. Our Proposed Approach • The Integration of – Fine-grained concept based guidance – Open student modeling with progressive zoom navigation support (zoomable open student modeling) – Problem sequencing 4
  5. 5. Related Work • Open Student Modeling • Progressive Zoom Navigation Support • Adaptive Problem Sequencing 5/19
  6. 6. Open Student Modeling • Student model – A critical component for any adaptive learning system • Open student model (over last 10 years) – Visualize student knowledge and progress – Support reflection, self-directed learning, and transparency – More attractive to students – Usually for individual student 6/19
  7. 7. Progressive Zoom Navigation Support 7/19 MinEr Liang. Progressive Zoom Navigation Support for Personalized E-learning [D]. School of Computer Science, Fudan University, 2013. • Google-like zoomable navigation • “Step-wise” integration of horizontal exploration and vertical immersion
  8. 8. Adaptive Problem Sequencing • One of the oldest techniques for intelligent educational systems – Associative mechanisms – Dynamic problem difficulty – Metadata – Fine-grained concept-level domain model 8/19
  9. 9. The KnowledgeZoom Final Exam Tool Knowledge Explorer Knowledge Maximizer 9/19
  10. 10. KnowledgeZoom The Java Final Exam Tool (1) • The Domain Model and the Learning Content • The Knowledge Explorer (KE) • The Knowledge Maximizer (KM) 10/19
  11. 11. The KnowledgeZoom Java Final Exam Tool (2) • The Domain Model and the Learning Content Java ontology: http://www.sis.pitt.edu/~paws/ont/java.owl Exercises indexed with the Java ontology 11/19
  12. 12. The KnowledgeZoom Java Final Exam Tool (3) • The Knowledge Explorer (KE) • A multi-level open student model is visualized with a zoomable Treemap • An overlay model of student’s Knowledge based on the Java ontology • A Treemap node corresponds to a concept in the ontology • Zooming-in to reveal the sub-tree in the ontology (aggregation) • Size: the number of exercises; color: the student’s knowledge level of the concept12
  13. 13. The KnowledgeZoom Java Final Exam Tool (4) • The knowledge Maximizer (KM) 1. How well is the student prepared to do the activity? 2. What is the impact of the activity? 3. Has the user already completed the activity? 4. How to rank the activity based on the above three factors? (sequencing) 13
  14. 14. Evaluation • Survey Design – A classroom study on a Java course, one week before the final exam – A undergraduate course at University of Pittsburgh – 14 students participated – Comparing KnowleddgeZoom with the other two tools: QuizGuide and Progressor+
  15. 15. Findings (Log Analysis I) Table I: System Usage Summary • Attempts: the total number of questions attempted • Success Rate: the percentage of correctly answered questions • Distinct Questions: the number of distinct attempted questions • Attempts per question: the number of attempts for doing a question • Sessions: the number of sessions the students worked with the systems 15
  16. 16. Findings (Log Analysis II) 16/19 TABLE II: NUMBER OF ATTEMPTS, SUCCESS RATES BY SYSTEM AND COMPLEXITY LEVEL Easy: A question with 15 or fewer concepts Moderate: A question with 16 to 90 concepts Complex: A question with 90 or more concepts
  17. 17. Finings (Student Feedback Analysis) Main results: • Ten valid feedback forms returned • 80% of the students considered the KZ system helpful as a whole (A11) • For KE, 70% considered its interface helpful to identify their knowledge weak points (A2) • For KM, about 80% of the students considered the ability of KM to generate quizzes that cover many concepts as helpful (A4) • Students expected better integration of KE and KM 17/19
  18. 18. Summary and Future Work • Main contribution – Provided an effective tool for students to prepare their final exam with zoomable open user modeling • Future work – To implement better connections between KE and KM by integrating concept zooming and question access – To further investigate how to represent more clearly the users’ knowledge with the Treemaps layout – To investigate the effect on students’ learning (engagement and awareness) 18/19
  19. 19. Thank you! • Questions and Feedback? 谢谢 cảm ơn bạn grazie 19/19

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