Presenter: Guan-Yu Chen 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

Outline Introduction Related works Methods Evaluations General discussion Conclusion

1. Introduction (1/2) One-to-one human tutoring Effective, but expensive. Intelligent Tutoring Systems, ITS Virtual teachers Difficult, labor-intensive, and time-consuming.

1. Introduction (2/2) Human intelligence Machine intelligence Be well trained in expert domain knowledge and tutoring knowledge. Machine intelligence Knowledge representation Knowledge elicitation Student modeling Adaptive tutoring

2. Related works Frequently asked questions, FAQs Virtual teaching assistant, VTA ProTracer  ProTracer 2.0

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.

3. Methods (2/3) Architecture of VTA in ProTracer 2.0.

3. Methods (3/3) Program tracing exercise interface of ProTracer 2.0.

4. Evaluations 3 research issues: Does the VTA help students detect and correct their program tracing errors? If yes, which feedback helped? Do the mechanisms (VTA) share the teacher tutoring load? Does teacher load reduce when more teacher hints for specific error situations are recorded?

4.1 Setting (1/3)

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.

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.

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.

4.3 Results and discussion (1/4)

4.3 Results and discussion (2/4)

4.3 Results and discussion (3/4)

4.3 Results and discussion (4/4)

5. General discussion Design issues of developing computer assisted learning systems. Trade-off issue of complementing machine intelligence and human intelligence.

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.

The End~ Thank you!!