Archive 10 academic presentations from ~/Downloads/slides/ (2014-2018)

- PhD defense slides (defense.key, Nov 2018) → phd_defense/
- Master's defense on MOOC peer evaluation (Dec 2014)
- ENGI 600 data-driven program repair (Apr 2015)
- COMP 600 data-driven program completion (Fall 2015, Spring 2016)
- COMP 600 Program Splicing presentation + feedback + response (Spring 2018)
- Program Splicing slides in .key and .pdf formats (Spring 2018)

Each file has a .md transcription with academic frontmatter.
Skipped www2015.pdf (duplicate of existing www15.zip) and syncthing conflict copy.

documents/academic/presentations/master_defense_2014.md

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+---
+category: academic
+type: academic
+person: Yanxin Lu
+date: 2014-12
+source: master_defense_2014.pptx
+---
+
+# Master's Thesis Defense: Improving Peer Evaluation Quality in MOOCs
+
+Yanxin Lu, December 2014. 40 slides.
+
+## Slide 2: Title
+Improving Peer Evaluation Quality in MOOCs — Yanxin Lu, December 2014
+
+## Slide 3–4: Summary
+- Motivations and Problems
+- Experiment
+- Statistical Analysis
+- Results
+- Conclusion
+
+## Slide 5: What is MOOC?
+
+## Slide 6: Intro to Interactive Programming in Python
+- Coursera course, 120,000 enrolled, 7,500 completed
+
+## Slide 7–8: Example Assignments
+- Stopwatch
+- Memory game
+
+## Slide 9: Grading Rubric for Stopwatch
+- 1 pt: Program successfully opens a frame with the stopwatch stopped
+- 2 pts: Program correctly draws number of successful stops at whole second vs total stops
+
+## Slide 10: Peer Grading
+- Example scores: 1, 9, 9, 9, 10 → Score = 9
+
+## Slide 11: Quality is Highly Variable
+- Lack of effort
+- Small bugs require more effort
+
+## Slide 12: Solution
+A web application where students can:
+- Look at other peer evaluations
+- Grade other peer evaluations
+
+## Slide 13: Findings
+- Grading evaluation has the strongest effect
+- The knowledge that one's own peer evaluation will be examined does not
+- Strong effect on peer evaluation quality simply because students know they are being studied
+
+## Slide 15: Experiment Summary
+- Sign up → Stopwatch → Memory
+
+## Slide 16: Sign up
+- Web consent form, three groups, prize
+- Nothing about specific study goals or what was being measured
+- 3,015 students
+
+## Slide 17: Three Groups
+- G1: Full treatment, grading + viewing
+- G2: Only viewing
+- G3: Control group
+- Size ratio G1:G2:G3 = 8:1:1
+
+## Slides 18–24: Experiment Phases
+- Submission Phase: Submit programs before deadline
+- Evaluation Phase: 1 self evaluation + 5 peer evaluations per rubric item (score + optional comment)
+- Grading Evaluation Phase (G1): Web app, per evaluation × rubric item → Good/Neutral/Bad
+- Viewing Phase (G1, G2): See number of good/neutral/bad ratings and their own evaluation
+
+## Slide 25: Statistics
+- Most evaluations are graded three times
+
+## Slide 27: Goal
+- Whether G1 does better grading compared to G2, G3 or both
+- Measuring quality: correct scores, comment length
+- Reject a set of null hypotheses
+
+## Slide 28: Bootstrapping
+- Simulation-based method using resampling with replacement
+- Statistically significant: p-value <= 0.05
+
+## Slide 30: Terms
+- Good programs: correct (machine grader verified)
+- Bad programs: incorrect
+- Bad job: incorrect grade OR no comment
+- Really bad job: incorrect grade AND no comment
+
+## Slides 31–38: Results
+Hypothesis tests on comment length, "bad job" fraction, and "really bad job" fraction across groups on good and bad programs.
+
+## Slide 39: Findings
+- Grading evaluation has the strongest positive effect
+- The knowledge that one's own peer evaluation will be examined does not
+- Strong Hawthorne effect: improvement simply from knowing they are being studied
+
+## Slide 40: Conclusion
+- A web application for peer evaluation assessment
+- Study has positive effect on quality of peer evaluations
+- Implications beyond peer evaluations