This document describes KnowledgeZoom, a concept-based exam study tool with zoomable open student modeling to help students efficiently prepare for final exams. It integrates fine-grained concept-based guidance, an open student model that progressively zooms between topics and sub-topics, and adaptive problem sequencing. The tool includes a Knowledge Explorer that visualizes students' knowledge gaps through a zoomable treemap, and a Knowledge Maximizer that sequences practice problems based on students' knowledge levels and impact. An evaluation with 14 students found the tool helped identify knowledge weaknesses and generated quizzes covering multiple concepts, though better integration between its components could further improve the student experience.

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. 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. 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. 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. Related Work
• Open Student Modeling
• Progressive Zoom Navigation Support
• Adaptive Problem Sequencing
5/19

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. 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. 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. The KnowledgeZoom Final Exam Tool
Knowledge
Explorer
Knowledge
Maximizer
9/19

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. 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. 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. 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. 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. 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. 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. 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. 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. Thank you!
• Questions and Feedback?
谢谢
cảm ơn bạn
grazie
19/19