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Complementary machine intelligence and
human intelligence in virtual teaching
assistant for tutoring program tracing
Computers & Education, 57 4 (2011), pp. 2303-2312.
Chih-Yueh Chou, Bau-Hung Huang, Chi-Jen Lin
Presenter: Guan-Yu Chen
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Outline
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3.
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Introduction
Related works
Methods
Evaluations
General discussion
Conclusion
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1. Introduction (1/2)
• One-to-one human tutoring
– Effective, but expensive.
• Intelligent Tutoring Systems, ITS
– Virtual teachers
– Difficult, labor-intensive, and time-consuming.
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1. Introduction (2/2)
• Human intelligence
– Be well trained in expert domain knowledge and
tutoring knowledge.
• Machine intelligence
–
–
–
–
Knowledge representation
Knowledge elicitation
Student modeling
Adaptive tutoring
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2. Related works
• Frequently asked questions, FAQs
• Virtual teaching assistant, VTA
– ProTracer  ProTracer 2.0
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3. Methods (1/3)
ProTracer 2.0 by 2 mechanisms:
• 1st: Applies machine intelligence to extend
human intelligence.
• 2nd: Applies machine intelligence to reuse
human intelligence.
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3. Methods (2/3)
Architecture of VTA in ProTracer 2.0.
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3. Methods (3/3)
Program tracing exercise interface of ProTracer 2.0.
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4. Evaluations
3 research issues:
1) Does the VTA help students detect and
correct their program tracing errors?
If yes, which feedback helped?
2) Do the mechanisms (VTA) share the teacher
tutoring load?
3) Does teacher load reduce when more teacher
hints for specific error situations are recorded?
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4.1 Setting (1/3)
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4.1 Setting (1/2)
Evaluation 1 (85 students, Computer Programming, 2009)
5 levels of hints:
• Level 1: indicate the position of errors.
• Level 2: indicate both the position and types of errors.
 Level 3: elaborate on the program code to help students.
 Level 4: instruct knowledge for correcting errors.
 Level 5: bottom-out hints to show how to correct errors
and explain the reason for corrections.
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4.1 Setting (2/2)
Evaluation 2 (64 students, Computer Programming, 2010)
5 levels of hints:
• Level 1: inform students if their answers incorrect.
• Level 2: indicate the position of errors.
• Level 3: indicate the position and types of errors.
 Level 4: elaborate on the program code or provide
knowledge needed to correct errors.
 Level 5: show how to correct the errors and explain the
reason for corrections.
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4.2 Measurement and analysis
• 1st issue: Likert scale (5 scores)
• 2nd issue:
– The accumulated ratio of highest level.
• 3rd issue:
– The ratio of new teacher-generated hints
in evaluation 2.
– The usage of old teacher-generated hints
from the data of evaluation 1.
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4.3 Results and discussion (1/4)
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4.3 Results and discussion (2/4)
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4.3 Results and discussion (3/4)
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4.3 Results and discussion (4/4)
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5. General discussion
• Design issues of developing computer assisted
learning systems.
• Trade-off issue of complementing machine
intelligence and human intelligence.
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6. Conclusion
• The results of evaluations confirm that these
two mechanisms significantly reduce teacher
load.
• These two mechanisms reduce the complexity
of developing machine intelligence.
• VTA is a feasible approach for the task domain
of program tracing.
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The End~
Thank you!!
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